├── .gitignore ├── Chapter01 ├── .DS_Store ├── .ipynb_checkpoints │ ├── Activity 1.01-checkpoint.ipynb │ ├── Activity 1.02-checkpoint.ipynb │ ├── Exercise 1.01-checkpoint.ipynb │ ├── Exercise 1.02-checkpoint.ipynb │ ├── Exercise 1.03-checkpoint.ipynb │ ├── Exercise 1.04-checkpoint.ipynb │ ├── Exercise 1.05-checkpoint.ipynb │ ├── Exercise 1.06-checkpoint.ipynb │ ├── Exercise 1.07-checkpoint.ipynb │ ├── Exercise 1.08-checkpoint.ipynb │ ├── Exercise 1.09-checkpoint.ipynb │ ├── Exercise 1.10-checkpoint.ipynb │ ├── Exercise 1.11-checkpoint.ipynb │ ├── Exercise 1.12-checkpoint.ipynb │ ├── Exercise 1.13-checkpoint.ipynb │ └── Exercise 1.14-checkpoint.ipynb ├── Activity 1.01 │ └── Activity 1.01.ipynb ├── Activity 1.02 │ └── Activity 1.02.ipynb ├── Exercise 1.01 │ └── Exercise 1.01.ipynb ├── Exercise 1.02 │ └── Exercise 1.02.ipynb ├── Exercise 1.03 │ └── Exercise 1.03.ipynb ├── Exercise 1.04 │ └── Exercise 1.04.ipynb ├── Exercise 1.05 │ └── Exercise 1.05.ipynb ├── Exercise 1.06 │ └── Exercise 1.06.ipynb ├── Exercise 1.07 │ └── Exercise 1.07.ipynb ├── Exercise 1.08 │ └── Exercise 1.08.ipynb ├── Exercise 1.09 │ └── Exercise 1.09.ipynb ├── Exercise 1.10 │ └── Exercise 1.10.ipynb ├── Exercise 1.11 │ └── Exercise 1.11.ipynb ├── Exercise 1.12 │ └── Exercise 1.12.ipynb ├── Exercise 1.13 │ └── Exercise 1.13.ipynb ├── Exercise 1.14 │ └── Exercise 1.14.ipynb ├── datasets │ ├── P&P Ch01 │ ├── car_models.csv │ └── ssn.csv └── unittest_chapter1 │ ├── .idea │ ├── inspectionProfiles │ │ └── profiles_settings.xml │ ├── misc.xml │ ├── modules.xml │ ├── unittest_chapter1.iml │ └── workspace.xml │ ├── .ipynb_checkpoints │ ├── Exercise 1-checkpoint.ipynb │ ├── Exercise 10-checkpoint.ipynb │ ├── Exercise 11-checkpoint.ipynb │ ├── Exercise 13-checkpoint.ipynb │ ├── Exercise 14-checkpoint.ipynb │ ├── Exercise 2-checkpoint.ipynb │ ├── Exercise 4-checkpoint.ipynb │ ├── Exercise 5-checkpoint.ipynb │ ├── Exercise 6-checkpoint.ipynb │ ├── Exercise 7-checkpoint.ipynb │ ├── Exercise 8-checkpoint.ipynb │ ├── Exercise 9-checkpoint.ipynb │ └── exercise 12-checkpoint.ipynb │ ├── Activity 1.01.ipynb │ ├── Activity 1.02.ipynb │ ├── Exercise 1.02.ipynb │ ├── Exercise 1.03.ipynb │ ├── Exercise 1.04.ipynb │ ├── Exercise 1.05.ipynb │ ├── Exercise 1.06.ipynb │ ├── Exercise 1.07.ipynb │ ├── Exercise 1.08.ipynb │ ├── Exercise 1.09.ipynb │ ├── Exercise 1.10.ipynb │ ├── Exercise 1.11.ipynb │ ├── Exercise 1.12.ipynb │ ├── Exercise 1.13.ipynb │ ├── requirements.txt │ └── test_chapter1.py ├── Chapter02 ├── .ipynb_checkpoints │ ├── Activity 2.01-checkpoint.ipynb │ ├── Activity 2.02-checkpoint.ipynb │ ├── Exercise 15-checkpoint.ipynb │ ├── Exercise 16-checkpoint.ipynb │ ├── Exercise 17-checkpoint.ipynb │ ├── Exercise 18-checkpoint.ipynb │ ├── Exercise 19-checkpoint.ipynb │ ├── Exercise 2.01-checkpoint.ipynb │ ├── Exercise 2.02-checkpoint.ipynb │ ├── Exercise 2.03-checkpoint.ipynb │ ├── Exercise 2.04-checkpoint.ipynb │ ├── Exercise 2.05-checkpoint.ipynb │ ├── Exercise 2.06-checkpoint.ipynb │ ├── Exercise 2.07-checkpoint.ipynb │ ├── Exercise 2.08-checkpoint.ipynb │ ├── Exercise 2.09-checkpoint.ipynb │ ├── Exercise 2.10-checkpoint.ipynb │ ├── Exercise 2.11-checkpoint.ipynb │ ├── Exercise 20-checkpoint.ipynb │ ├── Exercise 21-checkpoint.ipynb │ ├── Exercise 22-checkpoint.ipynb │ ├── Exercise 23-checkpoint.ipynb │ ├── Exercise 24-checkpoint.ipynb │ ├── Exercise 25-checkpoint.ipynb │ └── chapter2-checkpoint.ipynb ├── Activity 2.01 │ └── Activity 2.01.ipynb ├── Activity 2.02 │ └── Activity 2.02.ipynb ├── Exercise 2.01 │ └── Exercise 2.01.ipynb ├── Exercise 2.02 │ └── Exercise 2.02.ipynb ├── Exercise 2.03 │ └── Exercise 2.03.ipynb ├── Exercise 2.04 │ └── Exercise 2.04.ipynb ├── Exercise 2.05 │ └── Exercise 2.05.ipynb ├── Exercise 2.06 │ └── Exercise 2.06.ipynb ├── Exercise 2.07 │ └── Exercise 2.07.ipynb ├── Exercise 2.08 │ └── Exercise 2.08.ipynb ├── Exercise 2.09 │ └── Exercise 2.09.ipynb ├── Exercise 2.10 │ └── Exercise 2.10.ipynb ├── Exercise 2.11 │ └── Exercise 2.11.ipynb ├── datasets │ ├── AA.txt │ ├── Alice’s Adventures in Wonderland, by Lewis Carroll │ ├── data_temporary_files.txt │ ├── sales_record.csv │ └── users.json └── unittest_chapter2 │ ├── .ipynb_checkpoints │ ├── Exercise 15-checkpoint.ipynb │ ├── Exercise 16-checkpoint.ipynb │ ├── Exercise 17-checkpoint.ipynb │ ├── Exercise 18-checkpoint.ipynb │ ├── Exercise 19-checkpoint.ipynb │ ├── Exercise 20-checkpoint.ipynb │ ├── Exercise 21-checkpoint.ipynb │ └── Exercise 22-checkpoint.ipynb │ ├── Activity 2.01.ipynb │ ├── Activity 2.02.ipynb │ ├── Exercise 2.01.ipynb │ ├── Exercise 2.02.ipynb │ ├── Exercise 2.03.ipynb │ ├── Exercise 2.04.ipynb │ ├── Exercise 2.06.ipynb │ ├── Exercise 2.07.ipynb │ ├── Exercise 2.08.ipynb │ ├── Exercise 2.09.ipynb │ ├── requirements.txt │ └── test_chapter2.py ├── Chapter03 ├── .ipynb_checkpoints │ ├── Activity 3.01-checkpoint.ipynb │ ├── Exercise 26-checkpoint.ipynb │ ├── Exercise 27-checkpoint.ipynb │ ├── Exercise 28-checkpoint.ipynb │ ├── Exercise 29-checkpoint.ipynb │ ├── Exercise 3.01-checkpoint.ipynb │ ├── Exercise 3.02-checkpoint.ipynb │ ├── Exercise 3.03-checkpoint.ipynb │ ├── Exercise 3.08-checkpoint.ipynb │ ├── Exercise 3.09-checkpoint.ipynb │ ├── Exercise 3.11-checkpoint.ipynb │ ├── Exercise 3.13-checkpoint.ipynb │ ├── Exercise 3.14-checkpoint.ipynb │ ├── Exercise 3.15-checkpoint.ipynb │ ├── Exercise 3.16-checkpoint.ipynb │ ├── Exercise 3.17-checkpoint.ipynb │ ├── Exercise 3.18-checkpoint.ipynb │ ├── Exercise 3.19-checkpoint.ipynb │ ├── Exercise 3.20-checkpoint.ipynb │ ├── Exercise 3.21-checkpoint.ipynb │ ├── Exercise 3.22-checkpoint.ipynb │ ├── Exercise 30-checkpoint.ipynb │ ├── Exercise 31-checkpoint.ipynb │ ├── Exercise 32-checkpoint.ipynb │ ├── Exercise 34-checkpoint.ipynb │ ├── Exercise 35-checkpoint.ipynb │ ├── Exercise 37-checkpoint.ipynb │ ├── Exercise 40-checkpoint.ipynb │ ├── Exercise 41-checkpoint.ipynb │ ├── Exercise 42-checkpoint.ipynb │ ├── Exercise 43-checkpoint.ipynb │ ├── exercise 38-checkpoint.ipynb │ └── exercise 39-checkpoint.ipynb ├── Activity 3.01 │ └── Activity 3.01.ipynb ├── Exercise 3.01 │ └── Exercise 3.01.ipynb ├── Exercise 3.02 │ └── Exercise 3.02.ipynb ├── Exercise 3.03 │ └── Exercise 3.03.ipynb ├── Exercise 3.04 │ └── Exercise 3.04.ipynb ├── Exercise 3.05 │ └── Exercise 3.05.ipynb ├── Exercise 3.06 │ └── Exercise 3.06.ipynb ├── Exercise 3.07 │ └── Exercise 3.07.ipynb ├── Exercise 3.08 │ └── Exercise 3.08.ipynb ├── Exercise 3.09 │ └── Exercise 3.09.ipynb ├── Exercise 3.10 │ └── Exercise 3.10.ipynb ├── Exercise 3.11 │ └── Exercise 3.11.ipynb ├── Exercise 3.12 │ └── Exercise 3.12.ipynb ├── Exercise 3.13 │ └── Exercise 3.13.ipynb ├── Exercise 3.14 │ └── Exercise 3.14.ipynb ├── Exercise 3.15 │ └── Exercise 3.15.ipynb ├── Exercise 3.16 │ └── Exercise 3.16.ipynb ├── Exercise 3.17 │ └── Exercise 3.17.ipynb ├── Exercise 3.18 │ └── Exercise 3.18.ipynb ├── Exercise 3.19 │ └── Exercise 3.19.ipynb ├── Exercise 3.20 │ └── Exercise 3.20.ipynb ├── Exercise 3.21 │ └── Exercise 3.21.ipynb ├── Exercise 3.22 │ └── Exercise 3.22.ipynb ├── datasets │ ├── Boston_housing.csv │ ├── numbers.csv │ ├── numbers2.csv │ └── stock.csv └── unit_tests │ ├── .ipynb_checkpoints │ ├── Exercise 26-checkpoint.ipynb │ ├── Exercise 27-checkpoint.ipynb │ ├── Exercise 29-checkpoint.ipynb │ ├── Exercise 30-checkpoint.ipynb │ ├── Exercise 31-checkpoint.ipynb │ ├── Exercise 32-checkpoint.ipynb │ ├── Exercise 33-checkpoint.ipynb │ ├── Exercise 34-checkpoint.ipynb │ ├── Exercise 35-checkpoint.ipynb │ ├── Exercise 36-checkpoint.ipynb │ ├── Exercise 37-checkpoint.ipynb │ ├── Exercise 38-checkpoint.ipynb │ ├── Exercise 40-checkpoint.ipynb │ ├── Exercise 41-checkpoint.ipynb │ ├── Exercise 42-checkpoint.ipynb │ ├── Exercise 43-checkpoint.ipynb │ ├── exercise 28-checkpoint.ipynb │ └── exercise 39-checkpoint.ipynb │ ├── Activity 3.01.ipynb │ ├── Exercise 3.01.ipynb │ ├── Exercise 3.02.ipynb │ ├── Exercise 3.04.ipynb │ ├── Exercise 3.05.ipynb │ ├── Exercise 3.06.ipynb │ ├── Exercise 3.07.ipynb │ ├── Exercise 3.08.ipynb │ ├── Exercise 3.09.ipynb │ ├── Exercise 3.10.ipynb │ ├── Exercise 3.11.ipynb │ ├── Exercise 3.12.ipynb │ ├── Exercise 3.13.ipynb │ ├── Exercise 3.15.ipynb │ ├── Exercise 3.16.ipynb │ ├── Exercise 3.17.ipynb │ ├── Exercise 3.18.ipynb │ ├── exercise 3.03.ipynb │ └── exercise 3.14.ipynb ├── Chapter04 ├── .ipynb_checkpoints │ ├── Activity 4.01-checkpoint.ipynb │ ├── Exercise 4.01-checkpoint.ipynb │ ├── Exercise 4.02-checkpoint.ipynb │ ├── Exercise 4.07-checkpoint.ipynb │ ├── Exercise 48-checkpoint.ipynb │ ├── Exercise 49-checkpoint.ipynb │ ├── Exercise 50-checkpoint.ipynb │ ├── Exercise 51-checkpoint.ipynb │ ├── Exercise 52-checkpoint.ipynb │ ├── Exercise 53-checkpoint.ipynb │ ├── Exercise 54-checkpoint.ipynb │ ├── Exercise 55-checkpoint.ipynb │ ├── Exercise 56-checkpoint.ipynb │ ├── Exercise 57-checkpoint.ipynb │ ├── Exercise 58-checkpoint.ipynb │ └── Exercise 59-checkpoint.ipynb ├── Activity 4.01 │ ├── .ipynb_checkpoints │ │ └── Activity 4.01-checkpoint.ipynb │ └── Activity 4.01.ipynb ├── Exercise 4.01 │ ├── .ipynb_checkpoints │ │ └── Exercise 4.01-checkpoint.ipynb │ └── Exercise 4.01.ipynb ├── Exercise 4.02 │ ├── .ipynb_checkpoints │ │ └── Exercise 4.02-checkpoint.ipynb │ └── Exercise 4.02.ipynb ├── Exercise 4.03 │ ├── .ipynb_checkpoints │ │ └── Exercise 4.03-checkpoint.ipynb │ └── Exercise 4.03.ipynb ├── Exercise 4.04 │ ├── .ipynb_checkpoints │ │ └── Exercise 4.04-checkpoint.ipynb │ └── Exercise 4.04.ipynb ├── Exercise 4.05 │ ├── .ipynb_checkpoints │ │ └── Exercise 4.05-checkpoint.ipynb │ └── Exercise 4.05.ipynb ├── Exercise 4.06 │ ├── .ipynb_checkpoints │ │ └── Exercise 4.06-checkpoint.ipynb │ └── Exercise 4.06.ipynb ├── Exercise 4.07 │ ├── .ipynb_checkpoints │ │ └── Exercise 4.07-checkpoint.ipynb │ └── Exercise 4.07.ipynb ├── Exercise 4.08 │ ├── .ipynb_checkpoints │ │ └── Exercise 4.08-checkpoint.ipynb │ └── Exercise 4.08.ipynb ├── Exercise 4.09 │ ├── .ipynb_checkpoints │ │ └── Exercise 4.09-checkpoint.ipynb │ └── Exercise 4.09.ipynb ├── Exercise 4.10 │ ├── .ipynb_checkpoints │ │ └── Exercise 4.10-checkpoint.ipynb │ └── Exercise 4.10.ipynb ├── Exercise 4.11 │ ├── .ipynb_checkpoints │ │ └── Exercise 4.11-checkpoint.ipynb │ └── Exercise 4.11.ipynb ├── Exercise 4.12 │ ├── .ipynb_checkpoints │ │ └── Exercise 4.12-checkpoint.ipynb │ └── Exercise 4.12.ipynb ├── datasets │ ├── Sample - Superstore.xls │ ├── adult_income_data.csv │ └── adult_income_names.txt └── unit_tests │ ├── .ipynb_checkpoints │ ├── Exercise 48-checkpoint.ipynb │ ├── Exercise 49-checkpoint.ipynb │ ├── Exercise 50-checkpoint.ipynb │ ├── Exercise 51-checkpoint.ipynb │ ├── Exercise 52-checkpoint.ipynb │ ├── Exercise 53-checkpoint.ipynb │ ├── Exercise 54-checkpoint.ipynb │ ├── Exercise 55-checkpoint.ipynb │ ├── Exercise 56-checkpoint.ipynb │ ├── Exercise 57-checkpoint.ipynb │ ├── Exercise 58-checkpoint.ipynb │ ├── Exercise 59-checkpoint.ipynb │ ├── Exercise 70-checkpoint.ipynb │ └── Exercise 71-checkpoint.ipynb │ ├── Activity 4.01.ipynb │ ├── Exercise 4.01.ipynb │ ├── Exercise 4.02.ipynb │ ├── Exercise 4.03.ipynb │ ├── Exercise 4.04.ipynb │ ├── Exercise 4.05.ipynb │ ├── Exercise 4.06.ipynb │ ├── Exercise 4.07.ipynb │ ├── Exercise 4.08.ipynb │ ├── Exercise 4.09.ipynb │ ├── Exercise 4.10.ipynb │ ├── Exercise 4.11.ipynb │ ├── Exercise 4.12.ipynb │ ├── Exercise 4.13.ipynb │ └── Exercise 4.14.ipynb ├── Chapter05 ├── .DS_Store ├── .ipynb_checkpoints │ ├── Activity 5.01-checkpoint.ipynb │ ├── Exercise 5.01-checkpoint.ipynb │ ├── Exercise 5.02-checkpoint.ipynb │ ├── Exercise 5.03-checkpoint.ipynb │ ├── Exercise 5.04-checkpoint.ipynb │ ├── Exercise 5.05-checkpoint.ipynb │ ├── Exercise 5.06-checkpoint.ipynb │ ├── Exercise 5.07-checkpoint.ipynb │ ├── Exercise 5.08-checkpoint.ipynb │ ├── Exercise 5.09-checkpoint.ipynb │ ├── Exercise 5.10-checkpoint.ipynb │ ├── Exercise 5.11-checkpoint.ipynb │ ├── Exercise 5.12-checkpoint.ipynb │ └── Exercise 5.13-checkpoint.ipynb ├── Activity 5.01 │ ├── .ipynb_checkpoints │ │ └── Activity 5.01-checkpoint.ipynb │ └── Activity 5.01.ipynb ├── Exercise 5.01 │ ├── .ipynb_checkpoints │ │ └── Exercise 5.01-checkpoint.ipynb │ └── Exercise 5.01.ipynb ├── Exercise 5.02 │ ├── .ipynb_checkpoints │ │ └── Exercise 5.02-checkpoint.ipynb │ └── Exercise 5.02.ipynb ├── Exercise 5.03 │ ├── .ipynb_checkpoints │ │ └── Exercise 5.03-checkpoint.ipynb │ └── Exercise 5.03.ipynb ├── Exercise 5.04 │ ├── .ipynb_checkpoints │ │ └── Exercise 5.04-checkpoint.ipynb │ └── Exercise 5.04.ipynb ├── Exercise 5.05 │ ├── .ipynb_checkpoints │ │ └── Exercise 5.05-checkpoint.ipynb │ └── Exercise 5.05.ipynb ├── Exercise 5.06 │ ├── .ipynb_checkpoints │ │ └── Exercise 5.06-checkpoint.ipynb │ └── Exercise 5.06.ipynb ├── Exercise 5.07 │ ├── .ipynb_checkpoints │ │ └── Exercise 5.07-checkpoint.ipynb │ └── Exercise 5.07.ipynb ├── Exercise 5.08 │ ├── .ipynb_checkpoints │ │ └── Exercise 5.08-checkpoint.ipynb │ └── Exercise 5.08.ipynb ├── Exercise 5.09 │ ├── .ipynb_checkpoints │ │ └── Exercise 5.09-checkpoint.ipynb │ └── Exercise 5.09.ipynb ├── Exercise 5.10 │ ├── .ipynb_checkpoints │ │ └── Exercise 5.10-checkpoint.ipynb │ └── Exercise 5.10.ipynb ├── Exercise 5.11 │ ├── .ipynb_checkpoints │ │ └── Exercise 5.11-checkpoint.ipynb │ └── Exercise 5.11.ipynb ├── Exercise 5.12 │ ├── .ipynb_checkpoints │ │ └── Exercise 5.12-checkpoint.ipynb │ └── Exercise 5.12.ipynb ├── Exercise 5.13 │ ├── .ipynb_checkpoints │ │ └── Exercise 5.13-checkpoint.ipynb │ └── Exercise 5.13.ipynb ├── datasets │ ├── Boston_housing.csv │ ├── CSV_EX_1.csv │ ├── CSV_EX_2.csv │ ├── CSV_EX_3.csv │ ├── CSV_EX_blankline.csv │ ├── CSV_EX_skipfooter.csv │ ├── CSV_EX_skiprows.csv │ ├── Housing_data.pdf │ ├── Housing_data.xlsx │ ├── List of countries by GDP (nominal) - Wikipedia.htm │ ├── List of countries by GDP (nominal) - Wikipedia_files │ │ ├── 16px-Flag_of_Nepal.svg.png │ │ ├── 16px-Flag_of_Switzerland.svg.png │ │ ├── 18px-Flag_of_Niger.svg.png │ │ ├── 19px-Flag_of_Monaco.svg.png │ │ ├── 20px-Flag_of_Denmark.svg.png │ │ ├── 20px-Flag_of_Gabon.svg.png │ │ ├── 20px-Flag_of_Papua_New_Guinea.svg.png │ │ ├── 20px-Flag_of_San_Marino.svg.png │ │ ├── 20px-Padlock-silver.svg.png │ │ ├── 21px-Flag_of_Albania.svg.png │ │ ├── 21px-Flag_of_Iceland.svg.png │ │ ├── 21px-Flag_of_Israel.svg.png │ │ ├── 21px-Flag_of_Kosovo.svg.png │ │ ├── 21px-Flag_of_Norway.svg.png │ │ ├── 22px-Flag_of_Andorra.svg.png │ │ ├── 22px-Flag_of_Bolivia.svg.png │ │ ├── 22px-Flag_of_Brazil.svg.png │ │ ├── 23px-Flag_of_Afghanistan.svg.png │ │ ├── 23px-Flag_of_Algeria.svg.png │ │ ├── 23px-Flag_of_Angola.svg.png │ │ ├── 23px-Flag_of_Anguilla.svg.png │ │ ├── 23px-Flag_of_Antigua_and_Barbuda.svg.png │ │ ├── 23px-Flag_of_Argentina.svg.png │ │ ├── 23px-Flag_of_Armenia.svg.png │ │ ├── 23px-Flag_of_Aruba.svg.png │ │ ├── 23px-Flag_of_Australia.svg.png │ │ ├── 23px-Flag_of_Austria.svg.png │ │ ├── 23px-Flag_of_Azerbaijan.svg.png │ │ ├── 23px-Flag_of_Bahrain.svg.png │ │ ├── 23px-Flag_of_Bangladesh.svg.png │ │ ├── 23px-Flag_of_Barbados.svg.png │ │ ├── 23px-Flag_of_Belarus.svg.png │ │ ├── 23px-Flag_of_Belgium_(civil).svg.png │ │ ├── 23px-Flag_of_Belize.svg.png │ │ ├── 23px-Flag_of_Benin.svg.png │ │ ├── 23px-Flag_of_Bermuda.svg.png │ │ ├── 23px-Flag_of_Bhutan.svg.png │ │ ├── 23px-Flag_of_Bosnia_and_Herzegovina.svg.png │ │ ├── 23px-Flag_of_Botswana.svg.png │ │ ├── 23px-Flag_of_Brunei.svg.png │ │ ├── 23px-Flag_of_Bulgaria.svg.png │ │ ├── 23px-Flag_of_Burkina_Faso.svg.png │ │ ├── 23px-Flag_of_Cambodia.svg.png │ │ ├── 23px-Flag_of_Cameroon.svg.png │ │ ├── 23px-Flag_of_Cape_Verde.svg.png │ │ ├── 23px-Flag_of_Chile.svg.png │ │ ├── 23px-Flag_of_Colombia.svg.png │ │ ├── 23px-Flag_of_Costa_Rica.svg.png │ │ ├── 23px-Flag_of_Croatia.svg.png │ │ ├── 23px-Flag_of_Cuba.svg.png │ │ ├── 23px-Flag_of_Curaçao.svg.png │ │ ├── 23px-Flag_of_Cyprus.svg.png │ │ ├── 23px-Flag_of_Djibouti.svg.png │ │ ├── 23px-Flag_of_Dominica.svg.png │ │ ├── 23px-Flag_of_East_Timor.svg.png │ │ ├── 23px-Flag_of_Ecuador.svg.png │ │ ├── 23px-Flag_of_Egypt.svg.png │ │ ├── 23px-Flag_of_El_Salvador.svg.png │ │ ├── 23px-Flag_of_Eritrea.svg.png │ │ ├── 23px-Flag_of_Estonia.svg.png │ │ ├── 23px-Flag_of_Ethiopia.svg.png │ │ ├── 23px-Flag_of_Europe.svg.png │ │ ├── 23px-Flag_of_Fiji.svg.png │ │ ├── 23px-Flag_of_Finland.svg.png │ │ ├── 23px-Flag_of_France.svg.png │ │ ├── 23px-Flag_of_French_Polynesia.svg.png │ │ ├── 23px-Flag_of_Georgia.svg.png │ │ ├── 23px-Flag_of_Germany.svg.png │ │ ├── 23px-Flag_of_Ghana.svg.png │ │ ├── 23px-Flag_of_Greece.svg.png │ │ ├── 23px-Flag_of_Greenland.svg.png │ │ ├── 23px-Flag_of_Grenada.svg.png │ │ ├── 23px-Flag_of_Guatemala.svg.png │ │ ├── 23px-Flag_of_Guinea-Bissau.svg.png │ │ ├── 23px-Flag_of_Guinea.svg.png │ │ ├── 23px-Flag_of_Guyana.svg.png │ │ ├── 23px-Flag_of_Haiti.svg.png │ │ ├── 23px-Flag_of_Honduras.svg.png │ │ ├── 23px-Flag_of_Hong_Kong.svg.png │ │ ├── 23px-Flag_of_Hungary.svg.png │ │ ├── 23px-Flag_of_India.svg.png │ │ ├── 23px-Flag_of_Indonesia.svg.png │ │ ├── 23px-Flag_of_Iran.svg.png │ │ ├── 23px-Flag_of_Iraq.svg.png │ │ ├── 23px-Flag_of_Ireland.svg.png │ │ ├── 23px-Flag_of_Italy.svg.png │ │ ├── 23px-Flag_of_Jamaica.svg.png │ │ ├── 23px-Flag_of_Japan.svg.png │ │ ├── 23px-Flag_of_Jordan.svg.png │ │ ├── 23px-Flag_of_Kazakhstan.svg.png │ │ ├── 23px-Flag_of_Kenya.svg.png │ │ ├── 23px-Flag_of_Kiribati.svg.png │ │ ├── 23px-Flag_of_Kuwait.svg.png │ │ ├── 23px-Flag_of_Kyrgyzstan.svg.png │ │ ├── 23px-Flag_of_Laos.svg.png │ │ ├── 23px-Flag_of_Latvia.svg.png │ │ ├── 23px-Flag_of_Lebanon.svg.png │ │ ├── 23px-Flag_of_Lesotho.svg.png │ │ ├── 23px-Flag_of_Liberia.svg.png │ │ ├── 23px-Flag_of_Libya.svg.png │ │ ├── 23px-Flag_of_Liechtenstein.svg.png │ │ ├── 23px-Flag_of_Lithuania.svg.png │ │ ├── 23px-Flag_of_Luxembourg.svg.png │ │ ├── 23px-Flag_of_Macau.svg.png │ │ ├── 23px-Flag_of_Macedonia.svg.png │ │ ├── 23px-Flag_of_Madagascar.svg.png │ │ ├── 23px-Flag_of_Malawi.svg.png │ │ ├── 23px-Flag_of_Malaysia.svg.png │ │ ├── 23px-Flag_of_Maldives.svg.png │ │ ├── 23px-Flag_of_Mali.svg.png │ │ ├── 23px-Flag_of_Malta.svg.png │ │ ├── 23px-Flag_of_Mauritania.svg.png │ │ ├── 23px-Flag_of_Mauritius.svg.png │ │ ├── 23px-Flag_of_Mexico.svg.png │ │ ├── 23px-Flag_of_Moldova.svg.png │ │ ├── 23px-Flag_of_Mongolia.svg.png │ │ ├── 23px-Flag_of_Montenegro.svg.png │ │ ├── 23px-Flag_of_Montserrat.svg.png │ │ ├── 23px-Flag_of_Morocco.svg.png │ │ ├── 23px-Flag_of_Myanmar.svg.png │ │ ├── 23px-Flag_of_Namibia.svg.png │ │ ├── 23px-Flag_of_Nauru.svg.png │ │ ├── 23px-Flag_of_New_Zealand.svg.png │ │ ├── 23px-Flag_of_Nicaragua.svg.png │ │ ├── 23px-Flag_of_Nigeria.svg.png │ │ ├── 23px-Flag_of_North_Korea.svg.png │ │ ├── 23px-Flag_of_Oman.svg.png │ │ ├── 23px-Flag_of_Pakistan.svg.png │ │ ├── 23px-Flag_of_Palau.svg.png │ │ ├── 23px-Flag_of_Palestine.svg.png │ │ ├── 23px-Flag_of_Panama.svg.png │ │ ├── 23px-Flag_of_Paraguay.svg.png │ │ ├── 23px-Flag_of_Peru.svg.png │ │ ├── 23px-Flag_of_Poland.svg.png │ │ ├── 23px-Flag_of_Portugal.svg.png │ │ ├── 23px-Flag_of_Puerto_Rico.svg.png │ │ ├── 23px-Flag_of_Qatar.svg.png │ │ ├── 23px-Flag_of_Romania.svg.png │ │ ├── 23px-Flag_of_Russia.svg.png │ │ ├── 23px-Flag_of_Rwanda.svg.png │ │ ├── 23px-Flag_of_Saint_Kitts_and_Nevis.svg.png │ │ ├── 23px-Flag_of_Saint_Lucia.svg.png │ │ ├── 23px-Flag_of_Samoa.svg.png │ │ ├── 23px-Flag_of_Sao_Tome_and_Principe.svg.png │ │ ├── 23px-Flag_of_Saudi_Arabia.svg.png │ │ ├── 23px-Flag_of_Senegal.svg.png │ │ ├── 23px-Flag_of_Serbia.svg.png │ │ ├── 23px-Flag_of_Seychelles.svg.png │ │ ├── 23px-Flag_of_Sierra_Leone.svg.png │ │ ├── 23px-Flag_of_Singapore.svg.png │ │ ├── 23px-Flag_of_Sint_Maarten.svg.png │ │ ├── 23px-Flag_of_Slovakia.svg.png │ │ ├── 23px-Flag_of_Slovenia.svg.png │ │ ├── 23px-Flag_of_Somalia.svg.png │ │ ├── 23px-Flag_of_South_Africa.svg.png │ │ ├── 23px-Flag_of_South_Korea.svg.png │ │ ├── 23px-Flag_of_Spain.svg.png │ │ ├── 23px-Flag_of_Sri_Lanka.svg.png │ │ ├── 23px-Flag_of_Suriname.svg.png │ │ ├── 23px-Flag_of_Swaziland.svg.png │ │ ├── 23px-Flag_of_Sweden.svg.png │ │ ├── 23px-Flag_of_Syria.svg.png │ │ ├── 23px-Flag_of_Tajikistan.svg.png │ │ ├── 23px-Flag_of_Tanzania.svg.png │ │ ├── 23px-Flag_of_Thailand.svg.png │ │ ├── 23px-Flag_of_The_Gambia.svg.png │ │ ├── 23px-Flag_of_Togo.svg.png │ │ ├── 23px-Flag_of_Tonga.svg.png │ │ ├── 23px-Flag_of_Trinidad_and_Tobago.svg.png │ │ ├── 23px-Flag_of_Tunisia.svg.png │ │ ├── 23px-Flag_of_Turkey.svg.png │ │ ├── 23px-Flag_of_Turkmenistan.svg.png │ │ ├── 23px-Flag_of_Tuvalu.svg.png │ │ ├── 23px-Flag_of_Uganda.svg.png │ │ ├── 23px-Flag_of_Ukraine.svg.png │ │ ├── 23px-Flag_of_Uruguay.svg.png │ │ ├── 23px-Flag_of_Uzbekistan.svg.png │ │ ├── 23px-Flag_of_Vanuatu.svg.png │ │ ├── 23px-Flag_of_Venezuela.svg.png │ │ ├── 23px-Flag_of_Vietnam.svg.png │ │ ├── 23px-Flag_of_Yemen.svg.png │ │ ├── 23px-Flag_of_Zambia.svg.png │ │ ├── 23px-Flag_of_Zanzibar.svg.png │ │ ├── 23px-Flag_of_Zimbabwe.svg.png │ │ ├── 23px-Flag_of_the_Bahamas.svg.png │ │ ├── 23px-Flag_of_the_British_Virgin_Islands.svg.png │ │ ├── 23px-Flag_of_the_Cayman_Islands.svg.png │ │ ├── 23px-Flag_of_the_Comoros.svg.png │ │ ├── 23px-Flag_of_the_Cook_Islands.svg.png │ │ ├── 23px-Flag_of_the_Czech_Republic.svg.png │ │ ├── 23px-Flag_of_the_Dominican_Republic.svg.png │ │ ├── 23px-Flag_of_the_Marshall_Islands.svg.png │ │ ├── 23px-Flag_of_the_Netherlands.svg.png │ │ ├── 23px-Flag_of_the_Philippines.svg.png │ │ ├── 23px-Flag_of_the_Republic_of_China.svg.png │ │ ├── 23px-Flag_of_the_Republic_of_the_Congo.svg.png │ │ ├── 23px-Flag_of_the_Solomon_Islands.svg.png │ │ ├── 23px-Flag_of_the_United_Kingdom.svg.png │ │ ├── 23px-Flag_of_the_United_States.svg.png │ │ ├── 28px-Emblem-money.svg.png │ │ ├── 28px-Liste.svg.png │ │ ├── 45cabf722ffec5de02631bda1f6ccce0.png │ │ ├── Flag_of_Burundi.svg.png │ │ ├── Flag_of_Canada.svg.png │ │ ├── Flag_of_Chad.svg.png │ │ ├── Flag_of_Côte_d'Ivoire.svg.png │ │ ├── Flag_of_Equatorial_Guinea.svg.png │ │ ├── Flag_of_FLNKS.svg.png │ │ ├── Flag_of_Mozambique.svg.png │ │ ├── Flag_of_South_Sudan.svg.png │ │ ├── Flag_of_Sudan.svg.png │ │ ├── Flag_of_the_United_Arab_Emirates.svg.png │ │ ├── load(1).php │ │ ├── load(2).php │ │ ├── load(3).php │ │ ├── load.php │ │ ├── poweredby_mediawiki_88x31.png │ │ └── wikimedia-button.png │ ├── Table_EX_1.txt │ ├── Table_tab_separated.txt │ ├── movies.json │ ├── test.html │ └── test_output.xlsx └── unit_tests │ ├── .ipynb_checkpoints │ ├── Exercise 60-checkpoint.ipynb │ ├── Exercise 61-checkpoint.ipynb │ ├── Exercise 62-checkpoint.ipynb │ ├── Exercise 63-checkpoint.ipynb │ ├── Exercise 64-checkpoint.ipynb │ ├── Exercise 65-checkpoint.ipynb │ ├── Exercise 66-checkpoint.ipynb │ ├── Exercise 67-checkpoint.ipynb │ ├── Exercise 68-checkpoint.ipynb │ ├── Exercise 69-checkpoint.ipynb │ └── Exercise 72-checkpoint.ipynb │ ├── Exercise 5.01.ipynb │ ├── Exercise 61.ipynb │ ├── Exercise 62.ipynb │ ├── Exercise 63.ipynb │ ├── Exercise 64.ipynb │ ├── Exercise 65.ipynb │ ├── Exercise 66.ipynb │ ├── Exercise 67.ipynb │ ├── Exercise 68.ipynb │ ├── Exercise 69.ipynb │ └── Exercise 72.ipynb ├── Chapter06 ├── .ipynb_checkpoints │ ├── Activity 6.01-checkpoint.ipynb │ ├── Exercise 01-checkpoint.ipynb │ ├── Exercise 02-checkpoint.ipynb │ ├── Exercise 3-checkpoint.ipynb │ ├── Exercise 4-checkpoint.ipynb │ ├── Exercise 5-checkpoint.ipynb │ ├── Exercise 6-checkpoint.ipynb │ ├── Exercise 6.01-checkpoint.ipynb │ ├── Exercise 6.02-checkpoint.ipynb │ ├── Exercise 6.03-checkpoint.ipynb │ ├── Exercise 6.04-checkpoint.ipynb │ ├── Exercise 6.05-checkpoint.ipynb │ ├── Exercise 6.06-checkpoint.ipynb │ ├── Exercise 6.07-checkpoint.ipynb │ ├── Exercise 6.08-checkpoint.ipynb │ ├── Exercise 6.09-checkpoint.ipynb │ ├── Exercise 7-checkpoint.ipynb │ └── Exercise 8-checkpoint.ipynb ├── Activity 6.01 │ └── Activity 6.01.ipynb ├── Exercise 6.01 │ └── Exercise 6.01.ipynb ├── Exercise 6.02 │ └── Exercise 6.02.ipynb ├── Exercise 6.03 │ └── Exercise 6.03.ipynb ├── Exercise 6.04 │ └── Exercise 6.04.ipynb ├── Exercise 6.05 │ └── Exercise 6.05.ipynb ├── Exercise 6.06 │ └── Exercise 6.06.ipynb ├── Exercise 6.07 │ └── Exercise 6.07.ipynb ├── Exercise 6.08 │ └── Exercise 6.08.ipynb ├── Exercise 6.09 │ └── Exercise 6.09.ipynb ├── datasets │ ├── combinded_data.csv │ ├── dummy_data.csv │ ├── dummy_header.csv │ └── visit_data.csv └── unit_testing │ ├── Activity 6.01.ipynb │ ├── Exercise 6.01.ipynb │ ├── Exercise 6.02.ipynb │ ├── Exercise 6.03.ipynb │ ├── Exercise 6.04.ipynb │ ├── Exercise 6.05.ipynb │ ├── Exercise 6.06.ipynb │ ├── Exercise 6.07.ipynb │ └── Exercise 6.08.ipynb ├── Chapter07 ├── .ipynb_checkpoints │ ├── Activity 7.01-checkpoint.ipynb │ ├── Activity 7.02-checkpoint.ipynb │ ├── Exercise 7.01-checkpoint.ipynb │ ├── Exercise 7.02-checkpoint.ipynb │ ├── Exercise 7.03-checkpoint.ipynb │ ├── Exercise 7.04-checkpoint.ipynb │ ├── Exercise 7.05-checkpoint.ipynb │ ├── Exercise 7.06-checkpoint.ipynb │ ├── Exercise 7.07-checkpoint.ipynb │ ├── Exercise 7.08-checkpoint.ipynb │ ├── Exercise 7.09-checkpoint.ipynb │ ├── Exercise 7.10-checkpoint.ipynb │ ├── Exercise 7.11-checkpoint.ipynb │ ├── Exercise 7.12-checkpoint.ipynb │ ├── Exercise 7.13-checkpoint.ipynb │ ├── Exercise 7.14-checkpoint.ipynb │ ├── Exercise 7.15-checkpoint.ipynb │ ├── Exercise 7.16-checkpoint.ipynb │ ├── Exercise 7.17-checkpoint.ipynb │ ├── Exercise 7.18-checkpoint.ipynb │ ├── Exercise 7.19-checkpoint.ipynb │ ├── Exercise 7.20-checkpoint.ipynb │ ├── Exercise 7.21-checkpoint.ipynb │ ├── Exercise 7.22-checkpoint.ipynb │ ├── Exercise 7.23-checkpoint.ipynb │ ├── Exercise 7.24-checkpoint.ipynb │ ├── Exercise 7.25-checkpoint.ipynb │ └── Untitled-checkpoint.ipynb ├── Activity 7.01 │ └── Activity 7.01.ipynb ├── Activity 7.02 │ ├── APIkeys.json │ ├── Activity 7.02.ipynb │ └── Posters │ │ └── Titanic.jpg ├── Exercise 7.01 │ └── Exercise 7.01.ipynb ├── Exercise 7.02 │ └── Exercise 7.02.ipynb ├── Exercise 7.03 │ └── Exercise 7.03.ipynb ├── Exercise 7.04 │ └── Exercise 7.04.ipynb ├── Exercise 7.05 │ └── Exercise 7.05.ipynb ├── Exercise 7.06 │ └── Exercise 7.06.ipynb ├── Exercise 7.07 │ └── Exercise 7.07.ipynb ├── Exercise 7.08 │ └── Exercise 7.08.ipynb ├── Exercise 7.09 │ └── Exercise 7.09.ipynb ├── Exercise 7.10 │ ├── .ipynb_checkpoints │ │ └── Exercise 7.10-checkpoint.ipynb │ └── Exercise 7.10.ipynb ├── Exercise 7.11 │ ├── .ipynb_checkpoints │ │ └── Exercise 7.11-checkpoint.ipynb │ └── Exercise 7.11.ipynb ├── Exercise 7.12 │ └── Exercise 7.12.ipynb ├── Exercise 7.13 │ ├── .ipynb_checkpoints │ │ └── Exercise 7.13-checkpoint.ipynb │ └── Exercise 7.13.ipynb ├── Exercise 7.14 │ └── Exercise 7.14.ipynb ├── Exercise 7.15 │ └── Exercise 7.15.ipynb ├── Exercise 7.16 │ └── Exercise 7.16.ipynb ├── Exercise 7.17 │ └── Exercise 7.17.ipynb ├── Exercise 7.18 │ └── Exercise 7.18.ipynb ├── Exercise 7.19 │ ├── .ipynb_checkpoints │ │ └── Exercise 7.19-checkpoint.ipynb │ └── Exercise 7.19.ipynb ├── Exercise 7.20 │ └── Exercise 7.20.ipynb ├── Exercise 7.21 │ └── Exercise 7.21.ipynb ├── Exercise 7.22 │ └── Exercise 7.22.ipynb ├── Exercise 7.23 │ └── Exercise 7.23.ipynb ├── Exercise 7.24 │ └── Exercise 7.24.ipynb ├── Exercise 7.25 │ └── Exercise 7.25.ipynb ├── datasets │ └── xml1.xml └── unit_tests │ ├── .ipynb_checkpoints │ ├── Exercise 100-checkpoint.ipynb │ ├── Exercise 81-checkpoint.ipynb │ ├── Exercise 83-checkpoint.ipynb │ ├── Exercise 84-checkpoint.ipynb │ ├── Exercise 85-checkpoint.ipynb │ ├── Exercise 86-checkpoint.ipynb │ ├── Exercise 87-checkpoint.ipynb │ ├── Exercise 88-checkpoint.ipynb │ ├── Exercise 90-checkpoint.ipynb │ ├── Exercise 92-checkpoint.ipynb │ ├── Exercise 93-checkpoint.ipynb │ ├── Exercise 95-checkpoint.ipynb │ ├── Exercise 96-checkpoint.ipynb │ ├── Exercise 97-checkpoint.ipynb │ ├── Exercise 98-checkpoint.ipynb │ └── Exercise 99-checkpoint.ipynb │ ├── Activity 7.01.ipynb │ ├── Activity 7.02.ipynb │ ├── Exercise 7.01.ipynb │ ├── Exercise 7.02.ipynb │ ├── Exercise 7.03.ipynb │ ├── Exercise 7.04.ipynb │ ├── Exercise 7.05.ipynb │ ├── Exercise 7.06.ipynb │ ├── Exercise 7.07.ipynb │ ├── Exercise 7.08.ipynb │ ├── Exercise 7.09.ipynb │ ├── Exercise 7.10.ipynb │ ├── Exercise 7.11.ipynb │ ├── Exercise 7.12.ipynb │ ├── Exercise 7.13.ipynb │ ├── Exercise 7.14.ipynb │ ├── Exercise 7.15.ipynb │ └── Exercise 7.16.ipynb ├── Chapter08 ├── .ipynb_checkpoints │ ├── Activity 8.01-checkpoint.ipynb │ ├── Exercise 8.01-checkpoint.ipynb │ ├── Exercise 8.02-checkpoint.ipynb │ ├── Exercise 8.03-checkpoint.ipynb │ ├── Exercise 8.04-checkpoint.ipynb │ ├── Exercise 8.05-checkpoint.ipynb │ ├── Exercise 8.06-checkpoint.ipynb │ └── Exercise 8.07-checkpoint.ipynb ├── Activity 8.01 │ ├── Activity 8.01.ipynb │ └── petsdb ├── Exercise 8.01 │ └── Exercise 8.01.ipynb ├── Exercise 8.02 │ ├── .ipynb_checkpoints │ │ └── Exercise 8.02-checkpoint.ipynb │ └── Exercise 8.02.ipynb ├── Exercise 8.03 │ └── Exercise 8.03.ipynb ├── Exercise 8.04 │ └── Exercise 8.04.ipynb ├── Exercise 8.05 │ └── Exercise 8.05.ipynb ├── Exercise 8.06 │ └── Exercise 8.06.ipynb ├── Exercise 8.07 │ └── Exercise 8.07.ipynb ├── petsdb ├── petsdb.db └── unit_tests │ ├── .ipynb_checkpoints │ ├── Exercise 8.01-checkpoint.ipynb │ ├── Exercise 8.04-checkpoint.ipynb │ ├── Exercise 8.05-checkpoint.ipynb │ ├── Exercise 8.06-checkpoint.ipynb │ ├── Exercise 8.07-checkpoint.ipynb │ ├── exercise 8.02-checkpoint.ipynb │ └── exervise 8.03-checkpoint.ipynb │ ├── Activity8.01.ipynb │ ├── Exercise 8.01.ipynb │ ├── Exercise 8.03.ipynb │ ├── Exercise 8.04.ipynb │ ├── Exercise 8.05.ipynb │ ├── Exercise 8.06.ipynb │ ├── Exercise 8.07.ipynb │ └── exercise 8.02.ipynb ├── Chapter09 ├── .ipynb_checkpoints │ ├── Activity 9.01-checkpoint.ipynb │ ├── Activity 9.02-checkpoint.ipynb │ ├── Activity 9.03-checkpoint.ipynb │ ├── Activity 9.04-checkpoint.ipynb │ └── Activity_9.01-9.04-checkpoint.ipynb ├── Activity 9.01-9.04 │ └── Activity_9.01-9.04.ipynb ├── datasets │ ├── India_World_Bank_Info.csv │ └── SYB61_T07_Education.csv └── unit tests │ ├── Activity 9.01.ipynb │ ├── Activity 9.02.ipynb │ ├── Activity 9.03.ipynb │ └── Activity 9.04.ipynb ├── LICENSE ├── README.md └── requirements.txt /Chapter01/.DS_Store: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/PacktWorkshops/The-Data-Wrangling-Workshop/ddad20f8676602ac6624e72e802769fcaff45b0f/Chapter01/.DS_Store -------------------------------------------------------------------------------- /Chapter01/Exercise 1.14/Exercise 1.14.ipynb: -------------------------------------------------------------------------------- 1 | { 2 | "cells": [ 3 | { 4 | "cell_type": "code", 5 | "execution_count": 32, 6 | "metadata": {}, 7 | "outputs": [ 8 | { 9 | "data": { 10 | "text/plain": [ 11 | "['Name', ' Age', ' Sex', ' Address']" 12 | ] 13 | }, 14 | "execution_count": 32, 15 | "metadata": {}, 16 | "output_type": "execute_result" 17 | } 18 | ], 19 | "source": [ 20 | "str_1 = \"Name, Age, Sex, Address\" \n", 21 | "\n", 22 | "list_1 = str_1.split(\",\") \n", 23 | "\n", 24 | "list_1 " 25 | ] 26 | }, 27 | { 28 | "cell_type": "code", 29 | "execution_count": 34, 30 | "metadata": {}, 31 | "outputs": [ 32 | { 33 | "data": { 34 | "text/plain": [ 35 | "'Name | Age | Sex | Address'" 36 | ] 37 | }, 38 | "execution_count": 34, 39 | "metadata": {}, 40 | "output_type": "execute_result" 41 | } 42 | ], 43 | "source": [ 44 | "s = \" | \"\n", 45 | "s.join(list_1)" 46 | ] 47 | } 48 | ], 49 | "metadata": { 50 | "kernelspec": { 51 | "display_name": "Python 3", 52 | "language": "python", 53 | "name": "python3" 54 | }, 55 | "language_info": { 56 | "codemirror_mode": { 57 | "name": "ipython", 58 | "version": 3 59 | }, 60 | "file_extension": ".py", 61 | "mimetype": "text/x-python", 62 | "name": "python", 63 | "nbconvert_exporter": "python", 64 | "pygments_lexer": "ipython3", 65 | "version": "3.7.4" 66 | } 67 | }, 68 | "nbformat": 4, 69 | "nbformat_minor": 2 70 | } 71 | -------------------------------------------------------------------------------- /Chapter01/datasets/car_models.csv: -------------------------------------------------------------------------------- 1 | Escalade , X5 M,D150,Camaro,F350,Aurora,S8,E350,Tiburon,F-Series Super Duty 2 | -------------------------------------------------------------------------------- /Chapter01/datasets/ssn.csv: -------------------------------------------------------------------------------- 1 | 218-68-9955,165-73-3124,432-47-4043,563-93-1393,153-93-3401,670-09-7369,123-05-9652,812-13-2476,726-13-1007,825-05-4836 2 | -------------------------------------------------------------------------------- /Chapter01/unittest_chapter1/.idea/inspectionProfiles/profiles_settings.xml: -------------------------------------------------------------------------------- 1 | 2 | 3 | 6 | -------------------------------------------------------------------------------- /Chapter01/unittest_chapter1/.idea/misc.xml: -------------------------------------------------------------------------------- 1 | 2 | 3 | 4 | 6 | 7 | -------------------------------------------------------------------------------- /Chapter01/unittest_chapter1/.idea/modules.xml: -------------------------------------------------------------------------------- 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | -------------------------------------------------------------------------------- /Chapter01/unittest_chapter1/.idea/unittest_chapter1.iml: -------------------------------------------------------------------------------- 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9 | 10 | 11 | 13 | 14 | 16 | -------------------------------------------------------------------------------- /Chapter01/unittest_chapter1/.ipynb_checkpoints/Exercise 10-checkpoint.ipynb: -------------------------------------------------------------------------------- 1 | { 2 | "cells": [ 3 | { 4 | "cell_type": "code", 5 | "execution_count": null, 6 | "metadata": {}, 7 | "outputs": [], 8 | "source": [ 9 | "def test_exercise_10_1(x) -> bool:\n", 10 | " return x == {0: 0, 1: 1, 2: 4, 3: 9, 4: 16, 5: 25, 6: 36, 7: 49, 8: 64, 9: 81}\n" 11 | ] 12 | }, 13 | { 14 | "cell_type": "code", 15 | "execution_count": null, 16 | "metadata": {}, 17 | "outputs": [], 18 | "source": [ 19 | "def test_exercise_10_2(x) -> bool:\n", 20 | " return x == {'Tom': 100, 'Dick': 200, 'Harry': 300}\n" 21 | ] 22 | }, 23 | { 24 | "cell_type": "code", 25 | "execution_count": null, 26 | "metadata": {}, 27 | "outputs": [], 28 | "source": [ 29 | "def test_exercise_10_3(x) -> bool:\n", 30 | " return x == {'Tom': 100, 'Dick': 200, 'Harry': 300}\n" 31 | ] 32 | } 33 | ], 34 | "metadata": { 35 | "kernelspec": { 36 | "display_name": "Python 3", 37 | "language": "python", 38 | "name": "python3" 39 | }, 40 | "language_info": { 41 | "codemirror_mode": { 42 | "name": "ipython", 43 | "version": 3 44 | }, 45 | "file_extension": ".py", 46 | "mimetype": "text/x-python", 47 | "name": "python", 48 | "nbconvert_exporter": "python", 49 | "pygments_lexer": "ipython3", 50 | "version": "3.7.3" 51 | } 52 | }, 53 | "nbformat": 4, 54 | "nbformat_minor": 2 55 | } 56 | -------------------------------------------------------------------------------- /Chapter01/unittest_chapter1/.ipynb_checkpoints/Exercise 11-checkpoint.ipynb: -------------------------------------------------------------------------------- 1 | { 2 | "cells": [ 3 | { 4 | "cell_type": "code", 5 | "execution_count": null, 6 | "metadata": {}, 7 | "outputs": [], 8 | "source": [ 9 | "def test_exercise_11_1(x) -> bool:\n", 10 | " return x == ('1', '3', '5')" 11 | ] 12 | } 13 | ], 14 | "metadata": { 15 | "kernelspec": { 16 | "display_name": "Python 3", 17 | "language": "python", 18 | "name": "python3" 19 | }, 20 | "language_info": { 21 | "codemirror_mode": { 22 | "name": "ipython", 23 | "version": 3 24 | }, 25 | "file_extension": ".py", 26 | "mimetype": "text/x-python", 27 | "name": "python", 28 | "nbconvert_exporter": "python", 29 | "pygments_lexer": "ipython3", 30 | "version": "3.7.3" 31 | } 32 | }, 33 | "nbformat": 4, 34 | "nbformat_minor": 2 35 | } 36 | -------------------------------------------------------------------------------- /Chapter01/unittest_chapter1/.ipynb_checkpoints/Exercise 14-checkpoint.ipynb: -------------------------------------------------------------------------------- 1 | { 2 | "cells": [ 3 | { 4 | "cell_type": "code", 5 | "execution_count": null, 6 | "metadata": {}, 7 | "outputs": [], 8 | "source": [ 9 | "def test_exercise_13_4(x) -> bool:\n", 10 | " return x == ['Name', ' Age', ' Sex', ' Address']" 11 | ] 12 | }, 13 | { 14 | "cell_type": "code", 15 | "execution_count": null, 16 | "metadata": {}, 17 | "outputs": [], 18 | "source": [ 19 | "def test_exercise_13_4(x) -> bool:\n", 20 | " return x == 'Name | Age | Sex | Address'" 21 | ] 22 | } 23 | ], 24 | "metadata": { 25 | "kernelspec": { 26 | "display_name": "Python 3", 27 | "language": "python", 28 | "name": "python3" 29 | }, 30 | "language_info": { 31 | "codemirror_mode": { 32 | "name": "ipython", 33 | "version": 3 34 | }, 35 | "file_extension": ".py", 36 | "mimetype": "text/x-python", 37 | "name": "python", 38 | "nbconvert_exporter": "python", 39 | "pygments_lexer": "ipython3", 40 | "version": "3.7.3" 41 | } 42 | }, 43 | "nbformat": 4, 44 | "nbformat_minor": 2 45 | } 46 | -------------------------------------------------------------------------------- /Chapter01/unittest_chapter1/.ipynb_checkpoints/Exercise 4-checkpoint.ipynb: -------------------------------------------------------------------------------- 1 | { 2 | "cells": [ 3 | { 4 | "cell_type": "code", 5 | "execution_count": null, 6 | "metadata": {}, 7 | "outputs": [], 8 | "source": [ 9 | "def test_exercise_4__1(x) -> bool:\n", 10 | " return x == [20, 19, 18, 17, 16, 15, 14, 13, 12, 11, 10, 9, 8, 7, 6, 5, 4, 3, 2, 1, 0]" 11 | ] 12 | }, 13 | { 14 | "cell_type": "code", 15 | "execution_count": null, 16 | "metadata": {}, 17 | "outputs": [], 18 | "source": [ 19 | "def test_exercise_4__1(x) -> bool:\n", 20 | " return x == [20, 19, 18, 17, 16, 15, 14, 13, 12, 11, 10, 9, 8, 7, 6, 5, 4, 3, 2, 1, 0]" 21 | ] 22 | } 23 | ], 24 | "metadata": { 25 | "kernelspec": { 26 | "display_name": "Python 3", 27 | "language": "python", 28 | "name": "python3" 29 | }, 30 | "language_info": { 31 | "codemirror_mode": { 32 | "name": "ipython", 33 | "version": 3 34 | }, 35 | "file_extension": ".py", 36 | "mimetype": "text/x-python", 37 | "name": "python", 38 | "nbconvert_exporter": "python", 39 | "pygments_lexer": "ipython3", 40 | "version": "3.7.3" 41 | } 42 | }, 43 | "nbformat": 4, 44 | "nbformat_minor": 2 45 | } 46 | -------------------------------------------------------------------------------- /Chapter01/unittest_chapter1/.ipynb_checkpoints/Exercise 5-checkpoint.ipynb: -------------------------------------------------------------------------------- 1 | { 2 | "cells": [ 3 | { 4 | "cell_type": "code", 5 | "execution_count": null, 6 | "metadata": {}, 7 | "outputs": [], 8 | "source": [ 9 | "def test_exercise_5__1(x) -> bool:\n", 10 | " import sys\n", 11 | " if \"random\" not in sys.modules:\n", 12 | " return False\n", 13 | " else:\n", 14 | " return True" 15 | ] 16 | }, 17 | { 18 | "cell_type": "code", 19 | "execution_count": null, 20 | "metadata": {}, 21 | "outputs": [], 22 | "source": [ 23 | "def test_exercise_5__2(x) -> bool:\n", 24 | " return 100 == len(x)" 25 | ] 26 | } 27 | ], 28 | "metadata": { 29 | "kernelspec": { 30 | "display_name": "Python 3", 31 | "language": "python", 32 | "name": "python3" 33 | }, 34 | "language_info": { 35 | "codemirror_mode": { 36 | "name": "ipython", 37 | "version": 3 38 | }, 39 | "file_extension": ".py", 40 | "mimetype": "text/x-python", 41 | "name": "python", 42 | "nbconvert_exporter": "python", 43 | "pygments_lexer": "ipython3", 44 | "version": "3.7.3" 45 | } 46 | }, 47 | "nbformat": 4, 48 | "nbformat_minor": 2 49 | } 50 | -------------------------------------------------------------------------------- /Chapter01/unittest_chapter1/.ipynb_checkpoints/Exercise 8-checkpoint.ipynb: -------------------------------------------------------------------------------- 1 | { 2 | "cells": [ 3 | { 4 | "cell_type": "code", 5 | "execution_count": null, 6 | "metadata": {}, 7 | "outputs": [], 8 | "source": [ 9 | "def test_exercise_8__1(x) -> bool:\n", 10 | " return 100 == len(x)" 11 | ] 12 | }, 13 | { 14 | "cell_type": "code", 15 | "execution_count": null, 16 | "metadata": {}, 17 | "outputs": [], 18 | "source": [ 19 | "def test_exercise_8__2(x) -> bool:\n", 20 | " return 31 == len(x)" 21 | ] 22 | } 23 | ], 24 | "metadata": { 25 | "kernelspec": { 26 | "display_name": "Python 3", 27 | "language": "python", 28 | "name": "python3" 29 | }, 30 | "language_info": { 31 | "codemirror_mode": { 32 | "name": "ipython", 33 | "version": 3 34 | }, 35 | "file_extension": ".py", 36 | "mimetype": "text/x-python", 37 | "name": "python", 38 | "nbconvert_exporter": "python", 39 | "pygments_lexer": "ipython3", 40 | "version": "3.7.3" 41 | } 42 | }, 43 | "nbformat": 4, 44 | "nbformat_minor": 2 45 | } 46 | -------------------------------------------------------------------------------- /Chapter01/unittest_chapter1/.ipynb_checkpoints/exercise 12-checkpoint.ipynb: -------------------------------------------------------------------------------- 1 | { 2 | "cells": [ 3 | { 4 | "cell_type": "code", 5 | "execution_count": null, 6 | "metadata": {}, 7 | "outputs": [], 8 | "source": [ 9 | "def test_exercise_12_1(x) -> bool:\n", 10 | " return x == 'Hello World!'" 11 | ] 12 | }, 13 | { 14 | "cell_type": "code", 15 | "execution_count": null, 16 | "metadata": {}, 17 | "outputs": [], 18 | "source": [ 19 | "def test_exercise_12_3(x) -> bool:\n", 20 | " return x == 'o'" 21 | ] 22 | }, 23 | { 24 | "cell_type": "code", 25 | "execution_count": null, 26 | "metadata": {}, 27 | "outputs": [], 28 | "source": [ 29 | "def test_exercise_12_4(x) -> bool:\n", 30 | " return x == '!'" 31 | ] 32 | }, 33 | { 34 | "cell_type": "code", 35 | "execution_count": null, 36 | "metadata": {}, 37 | "outputs": [], 38 | "source": [ 39 | "def test_exercise_12_5(x) -> bool:\n", 40 | " return x == '!'" 41 | ] 42 | } 43 | ], 44 | "metadata": { 45 | "kernelspec": { 46 | "display_name": "Python 3", 47 | "language": "python", 48 | "name": "python3" 49 | }, 50 | "language_info": { 51 | "codemirror_mode": { 52 | "name": "ipython", 53 | "version": 3 54 | }, 55 | "file_extension": ".py", 56 | "mimetype": "text/x-python", 57 | "name": "python", 58 | "nbconvert_exporter": "python", 59 | "pygments_lexer": "ipython3", 60 | "version": "3.7.3" 61 | } 62 | }, 63 | "nbformat": 4, 64 | "nbformat_minor": 2 65 | } 66 | -------------------------------------------------------------------------------- /Chapter01/unittest_chapter1/Exercise 1.03.ipynb: -------------------------------------------------------------------------------- 1 | { 2 | "cells": [ 3 | { 4 | "cell_type": "code", 5 | "execution_count": null, 6 | "metadata": {}, 7 | "outputs": [], 8 | "source": [ 9 | "def test_exercise_4__1(x) -> bool:\n", 10 | " return x == [20, 19, 18, 17, 16, 15, 14, 13, 12, 11, 10, 9, 8, 7, 6, 5, 4, 3, 2, 1, 0]" 11 | ] 12 | }, 13 | { 14 | "cell_type": "code", 15 | "execution_count": null, 16 | "metadata": {}, 17 | "outputs": [], 18 | "source": [ 19 | "def test_exercise_4__1(x) -> bool:\n", 20 | " return x == [20, 19, 18, 17, 16, 15, 14, 13, 12, 11, 10, 9, 8, 7, 6, 5, 4, 3, 2, 1, 0]" 21 | ] 22 | } 23 | ], 24 | "metadata": { 25 | "kernelspec": { 26 | "display_name": "Python 3", 27 | "language": "python", 28 | "name": "python3" 29 | }, 30 | "language_info": { 31 | "codemirror_mode": { 32 | "name": "ipython", 33 | "version": 3 34 | }, 35 | "file_extension": ".py", 36 | "mimetype": "text/x-python", 37 | "name": "python", 38 | "nbconvert_exporter": "python", 39 | "pygments_lexer": "ipython3", 40 | "version": "3.7.3" 41 | } 42 | }, 43 | "nbformat": 4, 44 | "nbformat_minor": 2 45 | } 46 | -------------------------------------------------------------------------------- /Chapter01/unittest_chapter1/Exercise 1.04.ipynb: -------------------------------------------------------------------------------- 1 | { 2 | "cells": [ 3 | { 4 | "cell_type": "code", 5 | "execution_count": null, 6 | "metadata": {}, 7 | "outputs": [], 8 | "source": [ 9 | "def test_exercise_5__1(x) -> bool:\n", 10 | " import sys\n", 11 | " if \"random\" not in sys.modules:\n", 12 | " return False\n", 13 | " else:\n", 14 | " return True" 15 | ] 16 | }, 17 | { 18 | "cell_type": "code", 19 | "execution_count": null, 20 | "metadata": {}, 21 | "outputs": [], 22 | "source": [ 23 | "def test_exercise_5__2(x) -> bool:\n", 24 | " return 100 == len(x)" 25 | ] 26 | } 27 | ], 28 | "metadata": { 29 | "kernelspec": { 30 | "display_name": "Python 3", 31 | "language": "python", 32 | "name": "python3" 33 | }, 34 | "language_info": { 35 | "codemirror_mode": { 36 | "name": "ipython", 37 | "version": 3 38 | }, 39 | "file_extension": ".py", 40 | "mimetype": "text/x-python", 41 | "name": "python", 42 | "nbconvert_exporter": "python", 43 | "pygments_lexer": "ipython3", 44 | "version": "3.7.3" 45 | } 46 | }, 47 | "nbformat": 4, 48 | "nbformat_minor": 2 49 | } 50 | -------------------------------------------------------------------------------- /Chapter01/unittest_chapter1/Exercise 1.07.ipynb: -------------------------------------------------------------------------------- 1 | { 2 | "cells": [ 3 | { 4 | "cell_type": "code", 5 | "execution_count": null, 6 | "metadata": {}, 7 | "outputs": [], 8 | "source": [ 9 | "def test_exercise_8__1(x) -> bool:\n", 10 | " return 100 == len(x)" 11 | ] 12 | }, 13 | { 14 | "cell_type": "code", 15 | "execution_count": null, 16 | "metadata": {}, 17 | "outputs": [], 18 | "source": [ 19 | "def test_exercise_8__2(x) -> bool:\n", 20 | " return 31 == len(x)" 21 | ] 22 | } 23 | ], 24 | "metadata": { 25 | "kernelspec": { 26 | "display_name": "Python 3", 27 | "language": "python", 28 | "name": "python3" 29 | }, 30 | "language_info": { 31 | "codemirror_mode": { 32 | "name": "ipython", 33 | "version": 3 34 | }, 35 | "file_extension": ".py", 36 | "mimetype": "text/x-python", 37 | "name": "python", 38 | "nbconvert_exporter": "python", 39 | "pygments_lexer": "ipython3", 40 | "version": "3.7.3" 41 | } 42 | }, 43 | "nbformat": 4, 44 | "nbformat_minor": 2 45 | } 46 | -------------------------------------------------------------------------------- /Chapter01/unittest_chapter1/Exercise 1.09.ipynb: -------------------------------------------------------------------------------- 1 | { 2 | "cells": [ 3 | { 4 | "cell_type": "code", 5 | "execution_count": null, 6 | "metadata": {}, 7 | "outputs": [], 8 | "source": [ 9 | "def test_exercise_10_1(x) -> bool:\n", 10 | " return x == {0: 0, 1: 1, 2: 4, 3: 9, 4: 16, 5: 25, 6: 36, 7: 49, 8: 64, 9: 81}\n" 11 | ] 12 | }, 13 | { 14 | "cell_type": "code", 15 | "execution_count": null, 16 | "metadata": {}, 17 | "outputs": [], 18 | "source": [ 19 | "def test_exercise_10_2(x) -> bool:\n", 20 | " return x == {'Tom': 100, 'Dick': 200, 'Harry': 300}\n" 21 | ] 22 | }, 23 | { 24 | "cell_type": "code", 25 | "execution_count": null, 26 | "metadata": {}, 27 | "outputs": [], 28 | "source": [ 29 | "def test_exercise_10_3(x) -> bool:\n", 30 | " return x == {'Tom': 100, 'Dick': 200, 'Harry': 300}\n" 31 | ] 32 | } 33 | ], 34 | "metadata": { 35 | "kernelspec": { 36 | "display_name": "Python 3", 37 | "language": "python", 38 | "name": "python3" 39 | }, 40 | "language_info": { 41 | "codemirror_mode": { 42 | "name": "ipython", 43 | "version": 3 44 | }, 45 | "file_extension": ".py", 46 | "mimetype": "text/x-python", 47 | "name": "python", 48 | "nbconvert_exporter": "python", 49 | "pygments_lexer": "ipython3", 50 | "version": "3.7.3" 51 | } 52 | }, 53 | "nbformat": 4, 54 | "nbformat_minor": 2 55 | } 56 | -------------------------------------------------------------------------------- /Chapter01/unittest_chapter1/Exercise 1.10.ipynb: -------------------------------------------------------------------------------- 1 | { 2 | "cells": [ 3 | { 4 | "cell_type": "code", 5 | "execution_count": null, 6 | "metadata": {}, 7 | "outputs": [], 8 | "source": [ 9 | "def test_exercise_11_1(x) -> bool:\n", 10 | " return x == ('1', '3', '5')" 11 | ] 12 | } 13 | ], 14 | "metadata": { 15 | "kernelspec": { 16 | "display_name": "Python 3", 17 | "language": "python", 18 | "name": "python3" 19 | }, 20 | "language_info": { 21 | "codemirror_mode": { 22 | "name": "ipython", 23 | "version": 3 24 | }, 25 | "file_extension": ".py", 26 | "mimetype": "text/x-python", 27 | "name": "python", 28 | "nbconvert_exporter": "python", 29 | "pygments_lexer": "ipython3", 30 | "version": "3.7.3" 31 | } 32 | }, 33 | "nbformat": 4, 34 | "nbformat_minor": 2 35 | } 36 | -------------------------------------------------------------------------------- /Chapter01/unittest_chapter1/Exercise 1.11.ipynb: -------------------------------------------------------------------------------- 1 | { 2 | "cells": [ 3 | { 4 | "cell_type": "code", 5 | "execution_count": 4, 6 | "metadata": {}, 7 | "outputs": [], 8 | "source": [ 9 | "def test_exercise_11_1(x) -> bool:\n", 10 | " tupleE = \"1\", \"3\", \"5\" \n", 11 | " return x == tupleE" 12 | ] 13 | }, 14 | { 15 | "cell_type": "code", 16 | "execution_count": 3, 17 | "metadata": {}, 18 | "outputs": [], 19 | "source": [ 20 | "def test_exercise_12_2(x) -> bool:\n", 21 | " return x == '5'" 22 | ] 23 | }, 24 | { 25 | "cell_type": "code", 26 | "execution_count": 1, 27 | "metadata": {}, 28 | "outputs": [], 29 | "source": [ 30 | "def test_exercise_12_4a(x) -> bool:\n", 31 | " return x == '1'" 32 | ] 33 | }, 34 | { 35 | "cell_type": "code", 36 | "execution_count": 2, 37 | "metadata": {}, 38 | "outputs": [], 39 | "source": [ 40 | "def test_exercise_12_4b(x) -> bool:\n", 41 | " return x == '3'" 42 | ] 43 | } 44 | ], 45 | "metadata": { 46 | "kernelspec": { 47 | "display_name": "Python 3", 48 | "language": "python", 49 | "name": "python3" 50 | }, 51 | "language_info": { 52 | "codemirror_mode": { 53 | "name": "ipython", 54 | "version": 3 55 | }, 56 | "file_extension": ".py", 57 | "mimetype": "text/x-python", 58 | "name": "python", 59 | "nbconvert_exporter": "python", 60 | "pygments_lexer": "ipython3", 61 | "version": "3.7.3" 62 | } 63 | }, 64 | "nbformat": 4, 65 | "nbformat_minor": 2 66 | } 67 | -------------------------------------------------------------------------------- /Chapter01/unittest_chapter1/Exercise 1.12.ipynb: -------------------------------------------------------------------------------- 1 | { 2 | "cells": [ 3 | { 4 | "cell_type": "code", 5 | "execution_count": null, 6 | "metadata": {}, 7 | "outputs": [], 8 | "source": [ 9 | "def test_exercise_13_1(x) -> bool:\n", 10 | " return x == 'Hello World! I am learning data wrangling'" 11 | ] 12 | }, 13 | { 14 | "cell_type": "code", 15 | "execution_count": null, 16 | "metadata": {}, 17 | "outputs": [], 18 | "source": [ 19 | "def test_exercise_13_2(x) -> bool:\n", 20 | " return x == 'llo Worl'" 21 | ] 22 | }, 23 | { 24 | "cell_type": "code", 25 | "execution_count": null, 26 | "metadata": {}, 27 | "outputs": [], 28 | "source": [ 29 | "def test_exercise_13_3(x) -> bool:\n", 30 | " return x == 'd! I am learning data wrangling'" 31 | ] 32 | }, 33 | { 34 | "cell_type": "code", 35 | "execution_count": null, 36 | "metadata": {}, 37 | "outputs": [], 38 | "source": [ 39 | "def test_exercise_13_4(x) -> bool:\n", 40 | " return x == ' wran'" 41 | ] 42 | } 43 | ], 44 | "metadata": { 45 | "kernelspec": { 46 | "display_name": "Python 3", 47 | "language": "python", 48 | "name": "python3" 49 | }, 50 | "language_info": { 51 | "codemirror_mode": { 52 | "name": "ipython", 53 | "version": 3 54 | }, 55 | "file_extension": ".py", 56 | "mimetype": "text/x-python", 57 | "name": "python", 58 | "nbconvert_exporter": "python", 59 | "pygments_lexer": "ipython3", 60 | "version": "3.7.3" 61 | } 62 | }, 63 | "nbformat": 4, 64 | "nbformat_minor": 2 65 | } 66 | -------------------------------------------------------------------------------- /Chapter01/unittest_chapter1/Exercise 1.13.ipynb: -------------------------------------------------------------------------------- 1 | { 2 | "cells": [ 3 | { 4 | "cell_type": "code", 5 | "execution_count": null, 6 | "metadata": {}, 7 | "outputs": [], 8 | "source": [ 9 | "def test_exercise_13_4(x) -> bool:\n", 10 | " return x == ['Name', ' Age', ' Sex', ' Address']" 11 | ] 12 | }, 13 | { 14 | "cell_type": "code", 15 | "execution_count": null, 16 | "metadata": {}, 17 | "outputs": [], 18 | "source": [ 19 | "def test_exercise_13_4(x) -> bool:\n", 20 | " return x == 'Name | Age | Sex | Address'" 21 | ] 22 | } 23 | ], 24 | "metadata": { 25 | "kernelspec": { 26 | "display_name": "Python 3", 27 | "language": "python", 28 | "name": "python3" 29 | }, 30 | "language_info": { 31 | "codemirror_mode": { 32 | "name": "ipython", 33 | "version": 3 34 | }, 35 | "file_extension": ".py", 36 | "mimetype": "text/x-python", 37 | "name": "python", 38 | "nbconvert_exporter": "python", 39 | "pygments_lexer": "ipython3", 40 | "version": "3.7.3" 41 | } 42 | }, 43 | "nbformat": 4, 44 | "nbformat_minor": 2 45 | } 46 | -------------------------------------------------------------------------------- /Chapter01/unittest_chapter1/requirements.txt: -------------------------------------------------------------------------------- 1 | 2 | -------------------------------------------------------------------------------- /Chapter02/.ipynb_checkpoints/Exercise 2.09-checkpoint.ipynb: -------------------------------------------------------------------------------- 1 | { 2 | "cells": [ 3 | { 4 | "cell_type": "markdown", 5 | "metadata": {}, 6 | "source": [ 7 | "1. Open a file in binary mode: " 8 | ] 9 | }, 10 | { 11 | "cell_type": "code", 12 | "execution_count": 2, 13 | "metadata": {}, 14 | "outputs": [], 15 | "source": [ 16 | "fd = open(\"Alice's Adventures in Wonderland, by Lewis Carroll\", \n", 17 | "\n", 18 | " \"rb\") " 19 | ] 20 | }, 21 | { 22 | "cell_type": "code", 23 | "execution_count": 3, 24 | "metadata": {}, 25 | "outputs": [], 26 | "source": [ 27 | "fd.close() " 28 | ] 29 | }, 30 | { 31 | "cell_type": "code", 32 | "execution_count": null, 33 | "metadata": {}, 34 | "outputs": [], 35 | "source": [] 36 | } 37 | ], 38 | "metadata": { 39 | "kernelspec": { 40 | "display_name": "Python 3", 41 | "language": "python", 42 | "name": "python3" 43 | }, 44 | "language_info": { 45 | "codemirror_mode": { 46 | "name": "ipython", 47 | "version": 3 48 | }, 49 | "file_extension": ".py", 50 | "mimetype": "text/x-python", 51 | "name": "python", 52 | "nbconvert_exporter": "python", 53 | "pygments_lexer": "ipython3", 54 | "version": "3.7.3" 55 | } 56 | }, 57 | "nbformat": 4, 58 | "nbformat_minor": 2 59 | } 60 | -------------------------------------------------------------------------------- /Chapter02/Exercise 2.04/Exercise 2.04.ipynb: -------------------------------------------------------------------------------- 1 | { 2 | "cells": [ 3 | { 4 | "cell_type": "code", 5 | "execution_count": 73, 6 | "metadata": {}, 7 | "outputs": [], 8 | "source": [ 9 | "import math " 10 | ] 11 | }, 12 | { 13 | "cell_type": "code", 14 | "execution_count": 75, 15 | "metadata": {}, 16 | "outputs": [], 17 | "source": [ 18 | "def my_sine(): \n", 19 | " return lambda x: math.sin(math.radians(x)) \n", 20 | "\n", 21 | "def my_cosine(): \n", 22 | " return lambda x: math.cos(math.radians(x)) " 23 | ] 24 | }, 25 | { 26 | "cell_type": "code", 27 | "execution_count": 77, 28 | "metadata": {}, 29 | "outputs": [ 30 | { 31 | "data": { 32 | "text/plain": [ 33 | "1.0" 34 | ] 35 | }, 36 | "execution_count": 77, 37 | "metadata": {}, 38 | "output_type": "execute_result" 39 | } 40 | ], 41 | "source": [ 42 | "sine = my_sine() \n", 43 | "cosine = my_cosine() \n", 44 | "math.pow(sine(30), 2) + math.pow(cosine(30), 2) " 45 | ] 46 | } 47 | ], 48 | "metadata": { 49 | "kernelspec": { 50 | "display_name": "Python 3", 51 | "language": "python", 52 | "name": "python3" 53 | }, 54 | "language_info": { 55 | "codemirror_mode": { 56 | "name": "ipython", 57 | "version": 3 58 | }, 59 | "file_extension": ".py", 60 | "mimetype": "text/x-python", 61 | "name": "python", 62 | "nbconvert_exporter": "python", 63 | "pygments_lexer": "ipython3", 64 | "version": "3.7.4" 65 | } 66 | }, 67 | "nbformat": 4, 68 | "nbformat_minor": 2 69 | } 70 | -------------------------------------------------------------------------------- /Chapter02/Exercise 2.09/Exercise 2.09.ipynb: -------------------------------------------------------------------------------- 1 | { 2 | "cells": [ 3 | { 4 | "cell_type": "code", 5 | "execution_count": 1, 6 | "metadata": {}, 7 | "outputs": [], 8 | "source": [ 9 | "fd = open(\"../datasets/AA.txt\", \"rb\") " 10 | ] 11 | }, 12 | { 13 | "cell_type": "code", 14 | "execution_count": 2, 15 | "metadata": {}, 16 | "outputs": [], 17 | "source": [ 18 | "fd.close() " 19 | ] 20 | } 21 | ], 22 | "metadata": { 23 | "kernelspec": { 24 | "display_name": "Python 3", 25 | "language": "python", 26 | "name": "python3" 27 | }, 28 | "language_info": { 29 | "codemirror_mode": { 30 | "name": "ipython", 31 | "version": 3 32 | }, 33 | "file_extension": ".py", 34 | "mimetype": "text/x-python", 35 | "name": "python", 36 | "nbconvert_exporter": "python", 37 | "pygments_lexer": "ipython3", 38 | "version": "3.7.4" 39 | } 40 | }, 41 | "nbformat": 4, 42 | "nbformat_minor": 2 43 | } 44 | -------------------------------------------------------------------------------- /Chapter02/datasets/AA.txt: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/PacktWorkshops/The-Data-Wrangling-Workshop/ddad20f8676602ac6624e72e802769fcaff45b0f/Chapter02/datasets/AA.txt -------------------------------------------------------------------------------- /Chapter02/datasets/data_temporary_files.txt: -------------------------------------------------------------------------------- 1 | The capital of India is Delhi 2 | The capital of France is Paris 3 | The capital of UK is London 4 | The capital of USA is Washington 5 | The capital of China is Beijing 6 | The capital of Japan is Tokyo 7 | -------------------------------------------------------------------------------- /Chapter02/unittest_chapter2/.ipynb_checkpoints/Exercise 16-checkpoint.ipynb: -------------------------------------------------------------------------------- 1 | { 2 | "cells": [ 3 | { 4 | "cell_type": "code", 5 | "execution_count": null, 6 | "metadata": {}, 7 | "outputs": [], 8 | "source": [ 9 | "def test_exercise_16_1(x) -> bool:\n", 10 | " return x == []" 11 | ] 12 | } 13 | ], 14 | "metadata": { 15 | "kernelspec": { 16 | "display_name": "Python 3", 17 | "language": "python", 18 | "name": "python3" 19 | }, 20 | "language_info": { 21 | "codemirror_mode": { 22 | "name": "ipython", 23 | "version": 3 24 | }, 25 | "file_extension": ".py", 26 | "mimetype": "text/x-python", 27 | "name": "python", 28 | "nbconvert_exporter": "python", 29 | "pygments_lexer": "ipython3", 30 | "version": "3.7.3" 31 | } 32 | }, 33 | "nbformat": 4, 34 | "nbformat_minor": 2 35 | } 36 | -------------------------------------------------------------------------------- /Chapter02/unittest_chapter2/.ipynb_checkpoints/Exercise 19-checkpoint.ipynb: -------------------------------------------------------------------------------- 1 | { 2 | "cells": [ 3 | { 4 | "cell_type": "code", 5 | "execution_count": null, 6 | "metadata": {}, 7 | "outputs": [], 8 | "source": [ 9 | "def test_exercise_19_1(x) -> bool:\n", 10 | " return x == [('USA', 'Washington'),\n", 11 | " ('India', 'Delhi'),\n", 12 | " ('France', 'Paris'),\n", 13 | " ('UK', 'London')]" 14 | ] 15 | }, 16 | { 17 | "cell_type": "code", 18 | "execution_count": null, 19 | "metadata": {}, 20 | "outputs": [], 21 | "source": [ 22 | "def test_exercise_19_2(x) -> bool:\n", 23 | " return x == [('India', 'Delhi'),\n", 24 | " ('UK', 'London'),\n", 25 | " ('France', 'Paris'),\n", 26 | " ('USA', 'Washington')]" 27 | ] 28 | } 29 | ], 30 | "metadata": { 31 | "kernelspec": { 32 | "display_name": "Python 3", 33 | "language": "python", 34 | "name": "python3" 35 | }, 36 | "language_info": { 37 | "codemirror_mode": { 38 | "name": "ipython", 39 | "version": 3 40 | }, 41 | "file_extension": ".py", 42 | "mimetype": "text/x-python", 43 | "name": "python", 44 | "nbconvert_exporter": "python", 45 | "pygments_lexer": "ipython3", 46 | "version": "3.7.3" 47 | } 48 | }, 49 | "nbformat": 4, 50 | "nbformat_minor": 2 51 | } 52 | -------------------------------------------------------------------------------- /Chapter02/unittest_chapter2/.ipynb_checkpoints/Exercise 20-checkpoint.ipynb: -------------------------------------------------------------------------------- 1 | { 2 | "cells": [ 3 | { 4 | "cell_type": "code", 5 | "execution_count": null, 6 | "metadata": {}, 7 | "outputs": [], 8 | "source": [ 9 | "def test_exercise_20_1(x) -> bool:\n", 10 | " return x == ['Hello', 'there.', 'How', 'are', 'you', 'doing?']" 11 | ] 12 | }, 13 | { 14 | "cell_type": "code", 15 | "execution_count": null, 16 | "metadata": {}, 17 | "outputs": [], 18 | "source": [ 19 | "def test_exercise_20_2(x) -> bool:\n", 20 | " return x == ['How', 'are']" 21 | ] 22 | }, 23 | { 24 | "cell_type": "code", 25 | "execution_count": null, 26 | "metadata": {}, 27 | "outputs": [], 28 | "source": [ 29 | "def test_exercise_20_3(x) -> bool:\n", 30 | " return x == True" 31 | ] 32 | } 33 | ], 34 | "metadata": { 35 | "kernelspec": { 36 | "display_name": "Python 3", 37 | "language": "python", 38 | "name": "python3" 39 | }, 40 | "language_info": { 41 | "codemirror_mode": { 42 | "name": "ipython", 43 | "version": 3 44 | }, 45 | "file_extension": ".py", 46 | "mimetype": "text/x-python", 47 | "name": "python", 48 | "nbconvert_exporter": "python", 49 | "pygments_lexer": "ipython3", 50 | "version": "3.7.3" 51 | } 52 | }, 53 | "nbformat": 4, 54 | "nbformat_minor": 2 55 | } 56 | -------------------------------------------------------------------------------- /Chapter02/unittest_chapter2/.ipynb_checkpoints/Exercise 21-checkpoint.ipynb: -------------------------------------------------------------------------------- 1 | { 2 | "cells": [ 3 | { 4 | "cell_type": "code", 5 | "execution_count": null, 6 | "metadata": {}, 7 | "outputs": [], 8 | "source": [ 9 | "def test_exercise_21_1(x) -> bool:\n", 10 | " return x == range(0, 100000)" 11 | ] 12 | }, 13 | { 14 | "cell_type": "code", 15 | "execution_count": null, 16 | "metadata": {}, 17 | "outputs": [], 18 | "source": [ 19 | "def test_exercise_21_2(x) -> bool:\n", 20 | " return x == []" 21 | ] 22 | } 23 | ], 24 | "metadata": { 25 | "kernelspec": { 26 | "display_name": "Python 3", 27 | "language": "python", 28 | "name": "python3" 29 | }, 30 | "language_info": { 31 | "codemirror_mode": { 32 | "name": "ipython", 33 | "version": 3 34 | }, 35 | "file_extension": ".py", 36 | "mimetype": "text/x-python", 37 | "name": "python", 38 | "nbconvert_exporter": "python", 39 | "pygments_lexer": "ipython3", 40 | "version": "3.7.3" 41 | } 42 | }, 43 | "nbformat": 4, 44 | "nbformat_minor": 2 45 | } 46 | -------------------------------------------------------------------------------- /Chapter02/unittest_chapter2/.ipynb_checkpoints/Exercise 22-checkpoint.ipynb: -------------------------------------------------------------------------------- 1 | { 2 | "cells": [ 3 | { 4 | "cell_type": "code", 5 | "execution_count": null, 6 | "metadata": {}, 7 | "outputs": [], 8 | "source": [ 9 | "def test_exercise_22_1(x) -> bool:\n", 10 | " import sys\n", 11 | " if \"os\" in sys.modules:\n", 12 | " return False\n", 13 | " else:\n", 14 | " return True" 15 | ] 16 | }, 17 | { 18 | "cell_type": "code", 19 | "execution_count": null, 20 | "metadata": {}, 21 | "outputs": [], 22 | "source": [ 23 | "def test_exercise_22_2(x) -> bool:\n", 24 | " return x == 'MY_VAL'" 25 | ] 26 | } 27 | ], 28 | "metadata": { 29 | "kernelspec": { 30 | "display_name": "Python 3", 31 | "language": "python", 32 | "name": "python3" 33 | }, 34 | "language_info": { 35 | "codemirror_mode": { 36 | "name": "ipython", 37 | "version": 3 38 | }, 39 | "file_extension": ".py", 40 | "mimetype": "text/x-python", 41 | "name": "python", 42 | "nbconvert_exporter": "python", 43 | "pygments_lexer": "ipython3", 44 | "version": "3.7.3" 45 | } 46 | }, 47 | "nbformat": 4, 48 | "nbformat_minor": 2 49 | } 50 | -------------------------------------------------------------------------------- /Chapter02/unittest_chapter2/Activity 2.01.ipynb: -------------------------------------------------------------------------------- 1 | { 2 | "cells": [ 3 | { 4 | "cell_type": "code", 5 | "execution_count": null, 6 | "metadata": {}, 7 | "outputs": [], 8 | "source": [ 9 | "def test_exercise_2(x) -> bool:\n", 10 | " return x == \" \"" 11 | ] 12 | } 13 | ], 14 | "metadata": { 15 | "kernelspec": { 16 | "display_name": "Python 3", 17 | "language": "python", 18 | "name": "python3" 19 | }, 20 | "language_info": { 21 | "codemirror_mode": { 22 | "name": "ipython", 23 | "version": 3 24 | }, 25 | "file_extension": ".py", 26 | "mimetype": "text/x-python", 27 | "name": "python", 28 | "nbconvert_exporter": "python", 29 | "pygments_lexer": "ipython3", 30 | "version": "3.7.3" 31 | } 32 | }, 33 | "nbformat": 4, 34 | "nbformat_minor": 2 35 | } 36 | -------------------------------------------------------------------------------- /Chapter02/unittest_chapter2/Activity 2.02.ipynb: -------------------------------------------------------------------------------- 1 | { 2 | "cells": [ 3 | { 4 | "cell_type": "code", 5 | "execution_count": null, 6 | "metadata": {}, 7 | "outputs": [], 8 | "source": [ 9 | "def test_exercise_1(x) -> bool:\n", 10 | " import sys\n", 11 | " if \"itertools\" in sys.modules:\n", 12 | " return False\n", 13 | " else:\n", 14 | " return True" 15 | ] 16 | }, 17 | { 18 | "cell_type": "code", 19 | "execution_count": null, 20 | "metadata": {}, 21 | "outputs": [], 22 | "source": [ 23 | "def test_exercise_1(x) -> bool:\n", 24 | " import sys\n", 25 | " if \"zip_longest\" in sys.modules:\n", 26 | " return False\n", 27 | " else:\n", 28 | " return True" 29 | ] 30 | } 31 | ], 32 | "metadata": { 33 | "kernelspec": { 34 | "display_name": "Python 3", 35 | "language": "python", 36 | "name": "python3" 37 | }, 38 | "language_info": { 39 | "codemirror_mode": { 40 | "name": "ipython", 41 | "version": 3 42 | }, 43 | "file_extension": ".py", 44 | "mimetype": "text/x-python", 45 | "name": "python", 46 | "nbconvert_exporter": "python", 47 | "pygments_lexer": "ipython3", 48 | "version": "3.7.3" 49 | } 50 | }, 51 | "nbformat": 4, 52 | "nbformat_minor": 2 53 | } 54 | -------------------------------------------------------------------------------- /Chapter02/unittest_chapter2/Exercise 2.02.ipynb: -------------------------------------------------------------------------------- 1 | { 2 | "cells": [ 3 | { 4 | "cell_type": "code", 5 | "execution_count": null, 6 | "metadata": {}, 7 | "outputs": [], 8 | "source": [ 9 | "def test_exercise_16_1(x) -> bool:\n", 10 | " return x == []" 11 | ] 12 | } 13 | ], 14 | "metadata": { 15 | "kernelspec": { 16 | "display_name": "Python 3", 17 | "language": "python", 18 | "name": "python3" 19 | }, 20 | "language_info": { 21 | "codemirror_mode": { 22 | "name": "ipython", 23 | "version": 3 24 | }, 25 | "file_extension": ".py", 26 | "mimetype": "text/x-python", 27 | "name": "python", 28 | "nbconvert_exporter": "python", 29 | "pygments_lexer": "ipython3", 30 | "version": "3.7.3" 31 | } 32 | }, 33 | "nbformat": 4, 34 | "nbformat_minor": 2 35 | } 36 | -------------------------------------------------------------------------------- /Chapter02/unittest_chapter2/Exercise 2.06.ipynb: -------------------------------------------------------------------------------- 1 | { 2 | "cells": [ 3 | { 4 | "cell_type": "code", 5 | "execution_count": null, 6 | "metadata": {}, 7 | "outputs": [], 8 | "source": [ 9 | "def test_exercise_19_1(x) -> bool:\n", 10 | " return x == [('USA', 'Washington'),\n", 11 | " ('India', 'Delhi'),\n", 12 | " ('France', 'Paris'),\n", 13 | " ('UK', 'London')]" 14 | ] 15 | }, 16 | { 17 | "cell_type": "code", 18 | "execution_count": null, 19 | "metadata": {}, 20 | "outputs": [], 21 | "source": [ 22 | "def test_exercise_19_2(x) -> bool:\n", 23 | " return x == [('India', 'Delhi'),\n", 24 | " ('UK', 'London'),\n", 25 | " ('France', 'Paris'),\n", 26 | " ('USA', 'Washington')]" 27 | ] 28 | } 29 | ], 30 | "metadata": { 31 | "kernelspec": { 32 | "display_name": "Python 3", 33 | "language": "python", 34 | "name": "python3" 35 | }, 36 | "language_info": { 37 | "codemirror_mode": { 38 | "name": "ipython", 39 | "version": 3 40 | }, 41 | "file_extension": ".py", 42 | "mimetype": "text/x-python", 43 | "name": "python", 44 | "nbconvert_exporter": "python", 45 | "pygments_lexer": "ipython3", 46 | "version": "3.7.3" 47 | } 48 | }, 49 | "nbformat": 4, 50 | "nbformat_minor": 2 51 | } 52 | -------------------------------------------------------------------------------- /Chapter02/unittest_chapter2/Exercise 2.07.ipynb: -------------------------------------------------------------------------------- 1 | { 2 | "cells": [ 3 | { 4 | "cell_type": "code", 5 | "execution_count": null, 6 | "metadata": {}, 7 | "outputs": [], 8 | "source": [ 9 | "def test_exercise_20_1(x) -> bool:\n", 10 | " return x == ['Hello', 'there.', 'How', 'are', 'you', 'doing?']" 11 | ] 12 | }, 13 | { 14 | "cell_type": "code", 15 | "execution_count": null, 16 | "metadata": {}, 17 | "outputs": [], 18 | "source": [ 19 | "def test_exercise_20_2(x) -> bool:\n", 20 | " return x == ['How', 'are']" 21 | ] 22 | }, 23 | { 24 | "cell_type": "code", 25 | "execution_count": null, 26 | "metadata": {}, 27 | "outputs": [], 28 | "source": [ 29 | "def test_exercise_20_3(x) -> bool:\n", 30 | " return x == True" 31 | ] 32 | } 33 | ], 34 | "metadata": { 35 | "kernelspec": { 36 | "display_name": "Python 3", 37 | "language": "python", 38 | "name": "python3" 39 | }, 40 | "language_info": { 41 | "codemirror_mode": { 42 | "name": "ipython", 43 | "version": 3 44 | }, 45 | "file_extension": ".py", 46 | "mimetype": "text/x-python", 47 | "name": "python", 48 | "nbconvert_exporter": "python", 49 | "pygments_lexer": "ipython3", 50 | "version": "3.7.3" 51 | } 52 | }, 53 | "nbformat": 4, 54 | "nbformat_minor": 2 55 | } 56 | -------------------------------------------------------------------------------- /Chapter02/unittest_chapter2/Exercise 2.08.ipynb: -------------------------------------------------------------------------------- 1 | { 2 | "cells": [ 3 | { 4 | "cell_type": "code", 5 | "execution_count": null, 6 | "metadata": {}, 7 | "outputs": [], 8 | "source": [ 9 | "def test_exercise_21_1(x) -> bool:\n", 10 | " return x == range(0, 100000)" 11 | ] 12 | }, 13 | { 14 | "cell_type": "code", 15 | "execution_count": null, 16 | "metadata": {}, 17 | "outputs": [], 18 | "source": [ 19 | "def test_exercise_21_2(x) -> bool:\n", 20 | " return x == []" 21 | ] 22 | } 23 | ], 24 | "metadata": { 25 | "kernelspec": { 26 | "display_name": "Python 3", 27 | "language": "python", 28 | "name": "python3" 29 | }, 30 | "language_info": { 31 | "codemirror_mode": { 32 | "name": "ipython", 33 | "version": 3 34 | }, 35 | "file_extension": ".py", 36 | "mimetype": "text/x-python", 37 | "name": "python", 38 | "nbconvert_exporter": "python", 39 | "pygments_lexer": "ipython3", 40 | "version": "3.7.3" 41 | } 42 | }, 43 | "nbformat": 4, 44 | "nbformat_minor": 2 45 | } 46 | -------------------------------------------------------------------------------- /Chapter02/unittest_chapter2/Exercise 2.09.ipynb: -------------------------------------------------------------------------------- 1 | { 2 | "cells": [ 3 | { 4 | "cell_type": "code", 5 | "execution_count": null, 6 | "metadata": {}, 7 | "outputs": [], 8 | "source": [ 9 | "def test_exercise_22_1(x) -> bool:\n", 10 | " import sys\n", 11 | " if \"os\" in sys.modules:\n", 12 | " return False\n", 13 | " else:\n", 14 | " return True" 15 | ] 16 | }, 17 | { 18 | "cell_type": "code", 19 | "execution_count": null, 20 | "metadata": {}, 21 | "outputs": [], 22 | "source": [ 23 | "def test_exercise_22_2(x) -> bool:\n", 24 | " return x == 'MY_VAL'" 25 | ] 26 | } 27 | ], 28 | "metadata": { 29 | "kernelspec": { 30 | "display_name": "Python 3", 31 | "language": "python", 32 | "name": "python3" 33 | }, 34 | "language_info": { 35 | "codemirror_mode": { 36 | "name": "ipython", 37 | "version": 3 38 | }, 39 | "file_extension": ".py", 40 | "mimetype": "text/x-python", 41 | "name": "python", 42 | "nbconvert_exporter": "python", 43 | "pygments_lexer": "ipython3", 44 | "version": "3.7.3" 45 | } 46 | }, 47 | "nbformat": 4, 48 | "nbformat_minor": 2 49 | } 50 | -------------------------------------------------------------------------------- /Chapter02/unittest_chapter2/requirements.txt: -------------------------------------------------------------------------------- 1 | 2 | -------------------------------------------------------------------------------- /Chapter03/.ipynb_checkpoints/Exercise 29-checkpoint.ipynb: -------------------------------------------------------------------------------- 1 | { 2 | "cells": [ 3 | { 4 | "cell_type": "markdown", 5 | "metadata": {}, 6 | "source": [ 7 | "1." 8 | ] 9 | }, 10 | { 11 | "cell_type": "code", 12 | "execution_count": 1, 13 | "metadata": {}, 14 | "outputs": [], 15 | "source": [ 16 | "import pandas as pd" 17 | ] 18 | }, 19 | { 20 | "cell_type": "code", 21 | "execution_count": null, 22 | "metadata": {}, 23 | "outputs": [], 24 | "source": [] 25 | } 26 | ], 27 | "metadata": { 28 | "kernelspec": { 29 | "display_name": "Python 3", 30 | "language": "python", 31 | "name": "python3" 32 | }, 33 | "language_info": { 34 | "codemirror_mode": { 35 | "name": "ipython", 36 | "version": 3 37 | }, 38 | "file_extension": ".py", 39 | "mimetype": "text/x-python", 40 | "name": "python", 41 | "nbconvert_exporter": "python", 42 | "pygments_lexer": "ipython3", 43 | "version": "3.7.3" 44 | } 45 | }, 46 | "nbformat": 4, 47 | "nbformat_minor": 2 48 | } 49 | -------------------------------------------------------------------------------- /Chapter03/.ipynb_checkpoints/Exercise 3.15-checkpoint.ipynb: -------------------------------------------------------------------------------- 1 | { 2 | "cells": [ 3 | { 4 | "cell_type": "code", 5 | "execution_count": null, 6 | "metadata": {}, 7 | "outputs": [], 8 | "source": [ 9 | "# 25 rows and 4 columns\n", 10 | "import numpy as np\n", 11 | "import pandas as pd\n", 12 | "matrix_data = np.random.randint(1,100,100).reshape(25,4)\n", 13 | "column_headings = ['W','X','Y','Z']\n", 14 | "df = pd.DataFrame(data=matrix_data,columns=column_headings)\n" 15 | ] 16 | }, 17 | { 18 | "cell_type": "code", 19 | "execution_count": null, 20 | "metadata": {}, 21 | "outputs": [], 22 | "source": [ 23 | "df.head()" 24 | ] 25 | }, 26 | { 27 | "cell_type": "code", 28 | "execution_count": null, 29 | "metadata": {}, 30 | "outputs": [], 31 | "source": [ 32 | "df.head(8)" 33 | ] 34 | }, 35 | { 36 | "cell_type": "code", 37 | "execution_count": null, 38 | "metadata": {}, 39 | "outputs": [], 40 | "source": [ 41 | "df.tail(10)" 42 | ] 43 | } 44 | ], 45 | "metadata": { 46 | "kernelspec": { 47 | "display_name": "Python 3", 48 | "language": "python", 49 | "name": "python3" 50 | }, 51 | "language_info": { 52 | "codemirror_mode": { 53 | "name": "ipython", 54 | "version": 3 55 | }, 56 | "file_extension": ".py", 57 | "mimetype": "text/x-python", 58 | "name": "python", 59 | "nbconvert_exporter": "python", 60 | "pygments_lexer": "ipython3", 61 | "version": "3.7.3" 62 | } 63 | }, 64 | "nbformat": 4, 65 | "nbformat_minor": 2 66 | } 67 | -------------------------------------------------------------------------------- /Chapter03/.ipynb_checkpoints/Exercise 35-checkpoint.ipynb: -------------------------------------------------------------------------------- 1 | { 2 | "cells": [], 3 | "metadata": {}, 4 | "nbformat": 4, 5 | "nbformat_minor": 2 6 | } 7 | -------------------------------------------------------------------------------- /Chapter03/.ipynb_checkpoints/Exercise 43-checkpoint.ipynb: -------------------------------------------------------------------------------- 1 | { 2 | "cells": [], 3 | "metadata": {}, 4 | "nbformat": 4, 5 | "nbformat_minor": 2 6 | } 7 | -------------------------------------------------------------------------------- /Chapter03/.ipynb_checkpoints/exercise 39-checkpoint.ipynb: -------------------------------------------------------------------------------- 1 | { 2 | "cells": [], 3 | "metadata": {}, 4 | "nbformat": 4, 5 | "nbformat_minor": 2 6 | } 7 | -------------------------------------------------------------------------------- /Chapter03/datasets/numbers.csv: -------------------------------------------------------------------------------- 1 | 100 2 | 101 3 | 102 4 | 103 5 | 104 6 | 105 7 | 106 8 | 107 9 | 108 10 | 109 11 | 110 12 | 111 13 | 112 14 | 113 15 | 114 16 | 115 17 | 116 18 | 117 19 | 118 20 | 119 21 | 120 22 | 121 23 | 122 24 | 123 25 | 124 26 | 125 27 | 126 28 | 127 29 | 128 30 | 129 31 | 130 32 | 131 33 | 132 34 | 133 35 | 134 36 | 135 37 | 136 38 | 137 39 | 138 40 | 139 41 | 140 42 | 141 43 | 142 44 | 143 45 | 144 46 | 145 47 | 146 48 | 147 49 | 148 50 | -------------------------------------------------------------------------------- /Chapter03/datasets/numbers2.csv: -------------------------------------------------------------------------------- 1 | 1,2,3 2 | 4,5,6 3 | 7,8,9 4 | -------------------------------------------------------------------------------- /Chapter03/datasets/stock.csv: -------------------------------------------------------------------------------- 1 | Symbol,Price 2 | MMM,100 3 | AOS,101 4 | ABT,102 5 | ABBV,103 6 | ACN,104 7 | ATVI,105 8 | AYI,106 9 | ADBE,107 10 | AAP,108 11 | AMD,109 12 | AES,110 13 | AET,111 14 | AMG,112 15 | AFL,113 16 | A,114 17 | APD,115 18 | AKAM,116 19 | ALK,117 20 | ALB,118 21 | ARE,119 22 | ALXN,120 23 | ALGN,121 24 | ALLE,122 25 | AGN,123 26 | ADS,124 27 | LNT,125 28 | ALL,126 29 | GOOGL,127 30 | GOOG,128 31 | MO,129 32 | AMZN,130 33 | AEE,131 34 | AAL,132 35 | AEP,133 36 | AXP,134 37 | AIG,135 38 | AMT,136 39 | AWK,137 40 | AMP,138 41 | ABC,139 42 | AME,140 43 | AMGN,141 44 | APH,142 45 | APC,143 46 | ADI,144 47 | ANDV,145 48 | ANSS,146 49 | ANTM,147 50 | AON,148 51 | -------------------------------------------------------------------------------- /Chapter03/unit_tests/.ipynb_checkpoints/Exercise 43-checkpoint.ipynb: -------------------------------------------------------------------------------- 1 | { 2 | "cells": [ 3 | { 4 | "cell_type": "code", 5 | "execution_count": null, 6 | "metadata": {}, 7 | "outputs": [], 8 | "source": [] 9 | } 10 | ], 11 | "metadata": { 12 | "kernelspec": { 13 | "display_name": "Python 3", 14 | "language": "python", 15 | "name": "python3" 16 | }, 17 | "language_info": { 18 | "codemirror_mode": { 19 | "name": "ipython", 20 | "version": 3 21 | }, 22 | "file_extension": ".py", 23 | "mimetype": "text/x-python", 24 | "name": "python", 25 | "nbconvert_exporter": "python", 26 | "pygments_lexer": "ipython3", 27 | "version": "3.7.3" 28 | } 29 | }, 30 | "nbformat": 4, 31 | "nbformat_minor": 2 32 | } 33 | -------------------------------------------------------------------------------- /Chapter03/unit_tests/Exercise 3.06.ipynb: -------------------------------------------------------------------------------- 1 | { 2 | "cells": [ 3 | { 4 | "cell_type": "code", 5 | "execution_count": null, 6 | "metadata": {}, 7 | "outputs": [], 8 | "source": [ 9 | "import pandas as pd\n", 10 | "import numpy as np\n", 11 | "\n", 12 | "def test_exercise_31_1(x) -> bool:\n", 13 | " df = pd.read_csv(\"..\\numbers2.csv\",header=None) \n", 14 | " list_2D = df.values\n", 15 | " mat1 = np.array(list_2D) \n", 16 | " return x == mat1" 17 | ] 18 | }, 19 | { 20 | "cell_type": "code", 21 | "execution_count": null, 22 | "metadata": {}, 23 | "outputs": [], 24 | "source": [ 25 | "\n", 26 | "import numpy as np\n", 27 | "\n", 28 | "def test_exercise_31_2(x) -> bool:\n", 29 | " tuple_2D = np.array([(1.5,2,3), (4,5,6)]) \n", 30 | " mat_tuple = np.array(tuple_2D) \n", 31 | " return x == mat_tuple" 32 | ] 33 | }, 34 | { 35 | "cell_type": "code", 36 | "execution_count": null, 37 | "metadata": {}, 38 | "outputs": [], 39 | "source": [] 40 | } 41 | ], 42 | "metadata": { 43 | "kernelspec": { 44 | "display_name": "Python 3", 45 | "language": "python", 46 | "name": "python3" 47 | }, 48 | "language_info": { 49 | "codemirror_mode": { 50 | "name": "ipython", 51 | "version": 3 52 | }, 53 | "file_extension": ".py", 54 | "mimetype": "text/x-python", 55 | "name": "python", 56 | "nbconvert_exporter": "python", 57 | "pygments_lexer": "ipython3", 58 | "version": "3.7.3" 59 | } 60 | }, 61 | "nbformat": 4, 62 | "nbformat_minor": 2 63 | } 64 | -------------------------------------------------------------------------------- /Chapter03/unit_tests/Exercise 3.18.ipynb: -------------------------------------------------------------------------------- 1 | { 2 | "cells": [ 3 | { 4 | "cell_type": "code", 5 | "execution_count": null, 6 | "metadata": {}, 7 | "outputs": [], 8 | "source": [] 9 | } 10 | ], 11 | "metadata": { 12 | "kernelspec": { 13 | "display_name": "Python 3", 14 | "language": "python", 15 | "name": "python3" 16 | }, 17 | "language_info": { 18 | "codemirror_mode": { 19 | "name": "ipython", 20 | "version": 3 21 | }, 22 | "file_extension": ".py", 23 | "mimetype": "text/x-python", 24 | "name": "python", 25 | "nbconvert_exporter": "python", 26 | "pygments_lexer": "ipython3", 27 | "version": "3.7.3" 28 | } 29 | }, 30 | "nbformat": 4, 31 | "nbformat_minor": 2 32 | } 33 | -------------------------------------------------------------------------------- /Chapter04/.ipynb_checkpoints/Exercise 50-checkpoint.ipynb: -------------------------------------------------------------------------------- 1 | { 2 | "cells": [], 3 | "metadata": {}, 4 | "nbformat": 4, 5 | "nbformat_minor": 2 6 | } 7 | -------------------------------------------------------------------------------- /Chapter04/.ipynb_checkpoints/Exercise 56-checkpoint.ipynb: -------------------------------------------------------------------------------- 1 | { 2 | "cells": [ 3 | { 4 | "cell_type": "code", 5 | "execution_count": null, 6 | "metadata": {}, 7 | "outputs": [], 8 | "source": [] 9 | } 10 | ], 11 | "metadata": { 12 | "kernelspec": { 13 | "display_name": "Python 3", 14 | "language": "python", 15 | "name": "python3" 16 | }, 17 | "language_info": { 18 | "codemirror_mode": { 19 | "name": "ipython", 20 | "version": 3 21 | }, 22 | "file_extension": ".py", 23 | "mimetype": "text/x-python", 24 | "name": "python", 25 | "nbconvert_exporter": "python", 26 | "pygments_lexer": "ipython3", 27 | "version": "3.7.3" 28 | } 29 | }, 30 | "nbformat": 4, 31 | "nbformat_minor": 2 32 | } 33 | -------------------------------------------------------------------------------- /Chapter04/datasets/Sample - Superstore.xls: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/PacktWorkshops/The-Data-Wrangling-Workshop/ddad20f8676602ac6624e72e802769fcaff45b0f/Chapter04/datasets/Sample - Superstore.xls -------------------------------------------------------------------------------- /Chapter04/datasets/adult_income_names.txt: -------------------------------------------------------------------------------- 1 | age: continuous. 2 | 3 | workclass: Private, Self-emp-not-inc, Self-emp-inc, Federal-gov, Local-gov, State-gov, Without-pay, Never-worked. 4 | 5 | fnlwgt: continuous. 6 | 7 | education: Bachelors, Some-college, 11th, HS-grad, Prof-school, Assoc-acdm, Assoc-voc, 9th, 7th-8th, 12th, Masters, 1st-4th, 10th, Doctorate, 5th-6th, Preschool. 8 | 9 | education-num: continuous. 10 | 11 | marital-status: Married-civ-spouse, Divorced, Never-married, Separated, Widowed, Married-spouse-absent, Married-AF-spouse. 12 | 13 | occupation: Tech-support, Craft-repair, Other-service, Sales, Exec-managerial, Prof-specialty, Handlers-cleaners, Machine-op-inspct, Adm-clerical, Farming-fishing, Transport-moving, Priv-house-serv, Protective-serv, Armed-Forces. 14 | 15 | relationship: Wife, Own-child, Husband, Not-in-family, Other-relative, Unmarried. 16 | 17 | sex: Female, Male. 18 | 19 | capital-gain: continuous. 20 | 21 | capital-loss: continuous. 22 | 23 | hours-per-week: continuous. 24 | 25 | native-country: United-States, Cambodia, England, Puerto-Rico, Canada, Germany, Outlying-US(Guam-USVI-etc), India, Japan, Greece, South, China, Cuba, Iran, Honduras, Philippines, Italy, Poland, Jamaica, Vietnam, Mexico, Portugal, Ireland, France, Dominican-Republic, Laos, Ecuador, Taiwan, Haiti, Columbia, Hungary, Guatemala, Nicaragua, Scotland, Thailand, Yugoslavia, El-Salvador, Trinadad&Tobago, Peru, Hong, Holand-Netherlands. -------------------------------------------------------------------------------- /Chapter04/unit_tests/.ipynb_checkpoints/Exercise 49-checkpoint.ipynb: -------------------------------------------------------------------------------- 1 | { 2 | "cells": [ 3 | { 4 | "cell_type": "code", 5 | "execution_count": 1, 6 | "metadata": {}, 7 | "outputs": [], 8 | "source": [ 9 | "def test_exercise_48_1(x) -> bool:\n", 10 | " import numpy as np \n", 11 | " import pandas as pd \n", 12 | " import matplotlib.pyplot as plt \n", 13 | " df = pd.read_excel(\"../Sample - Superstore.xls\") \n", 14 | " return df['State'].unique() == x\n", 15 | " " 16 | ] 17 | }, 18 | { 19 | "cell_type": "code", 20 | "execution_count": 3, 21 | "metadata": {}, 22 | "outputs": [], 23 | "source": [ 24 | "def test_exercise_48_2(x) -> bool:\n", 25 | " return 49 == x" 26 | ] 27 | } 28 | ], 29 | "metadata": { 30 | "kernelspec": { 31 | "display_name": "Python 3", 32 | "language": "python", 33 | "name": "python3" 34 | }, 35 | "language_info": { 36 | "codemirror_mode": { 37 | "name": "ipython", 38 | "version": 3 39 | }, 40 | "file_extension": ".py", 41 | "mimetype": "text/x-python", 42 | "name": "python", 43 | "nbconvert_exporter": "python", 44 | "pygments_lexer": "ipython3", 45 | "version": "3.7.3" 46 | } 47 | }, 48 | "nbformat": 4, 49 | "nbformat_minor": 2 50 | } 51 | -------------------------------------------------------------------------------- /Chapter04/unit_tests/.ipynb_checkpoints/Exercise 71-checkpoint.ipynb: -------------------------------------------------------------------------------- 1 | { 2 | "cells": [ 3 | { 4 | "cell_type": "code", 5 | "execution_count": 1, 6 | "metadata": {}, 7 | "outputs": [ 8 | { 9 | "name": "stdout", 10 | "output_type": "stream", 11 | "text": [ 12 | "Requirement already satisfied: openpyxl in /home/blipp/anaconda3/lib/python3.7/site-packages (2.6.2)\r\n", 13 | "Requirement already satisfied: jdcal in /home/blipp/anaconda3/lib/python3.7/site-packages (from openpyxl) (1.4.1)\r\n", 14 | "Requirement already satisfied: et-xmlfile in /home/blipp/anaconda3/lib/python3.7/site-packages (from openpyxl) (1.0.1)\r\n" 15 | ] 16 | } 17 | ], 18 | "source": [ 19 | "def test_exercise_71_2(x) -> bool:\n", 20 | " return \"pandas.io.excel._XlsxWriter\" in x " 21 | ] 22 | }, 23 | { 24 | "cell_type": "code", 25 | "execution_count": null, 26 | "metadata": {}, 27 | "outputs": [], 28 | "source": [] 29 | } 30 | ], 31 | "metadata": { 32 | "kernelspec": { 33 | "display_name": "Python 3", 34 | "language": "python", 35 | "name": "python3" 36 | }, 37 | "language_info": { 38 | "codemirror_mode": { 39 | "name": "ipython", 40 | "version": 3 41 | }, 42 | "file_extension": ".py", 43 | "mimetype": "text/x-python", 44 | "name": "python", 45 | "nbconvert_exporter": "python", 46 | "pygments_lexer": "ipython3", 47 | "version": "3.7.3" 48 | } 49 | }, 50 | "nbformat": 4, 51 | "nbformat_minor": 2 52 | } 53 | -------------------------------------------------------------------------------- /Chapter04/unit_tests/Exercise 4.02.ipynb: -------------------------------------------------------------------------------- 1 | { 2 | "cells": [ 3 | { 4 | "cell_type": "code", 5 | "execution_count": 1, 6 | "metadata": {}, 7 | "outputs": [], 8 | "source": [ 9 | "def test_exercise_48_1(x) -> bool:\n", 10 | " import numpy as np \n", 11 | " import pandas as pd \n", 12 | " import matplotlib.pyplot as plt \n", 13 | " df = pd.read_excel(\"../Sample - Superstore.xls\") \n", 14 | " return df['State'].unique() == x\n", 15 | " " 16 | ] 17 | }, 18 | { 19 | "cell_type": "code", 20 | "execution_count": 3, 21 | "metadata": {}, 22 | "outputs": [], 23 | "source": [ 24 | "def test_exercise_48_2(x) -> bool:\n", 25 | " return 49 == x" 26 | ] 27 | } 28 | ], 29 | "metadata": { 30 | "kernelspec": { 31 | "display_name": "Python 3", 32 | "language": "python", 33 | "name": "python3" 34 | }, 35 | "language_info": { 36 | "codemirror_mode": { 37 | "name": "ipython", 38 | "version": 3 39 | }, 40 | "file_extension": ".py", 41 | "mimetype": "text/x-python", 42 | "name": "python", 43 | "nbconvert_exporter": "python", 44 | "pygments_lexer": "ipython3", 45 | "version": "3.7.3" 46 | } 47 | }, 48 | "nbformat": 4, 49 | "nbformat_minor": 2 50 | } 51 | -------------------------------------------------------------------------------- /Chapter04/unit_tests/Exercise 4.14.ipynb: -------------------------------------------------------------------------------- 1 | { 2 | "cells": [ 3 | { 4 | "cell_type": "code", 5 | "execution_count": 1, 6 | "metadata": {}, 7 | "outputs": [ 8 | { 9 | "name": "stdout", 10 | "output_type": "stream", 11 | "text": [ 12 | "Requirement already satisfied: openpyxl in /home/blipp/anaconda3/lib/python3.7/site-packages (2.6.2)\r\n", 13 | "Requirement already satisfied: jdcal in /home/blipp/anaconda3/lib/python3.7/site-packages (from openpyxl) (1.4.1)\r\n", 14 | "Requirement already satisfied: et-xmlfile in /home/blipp/anaconda3/lib/python3.7/site-packages (from openpyxl) (1.0.1)\r\n" 15 | ] 16 | } 17 | ], 18 | "source": [ 19 | "def test_exercise_71_2(x) -> bool:\n", 20 | " return \"pandas.io.excel._XlsxWriter\" in x " 21 | ] 22 | }, 23 | { 24 | "cell_type": "code", 25 | "execution_count": null, 26 | "metadata": {}, 27 | "outputs": [], 28 | "source": [] 29 | } 30 | ], 31 | "metadata": { 32 | "kernelspec": { 33 | "display_name": "Python 3", 34 | "language": "python", 35 | "name": "python3" 36 | }, 37 | "language_info": { 38 | "codemirror_mode": { 39 | "name": "ipython", 40 | "version": 3 41 | }, 42 | "file_extension": ".py", 43 | "mimetype": "text/x-python", 44 | "name": "python", 45 | "nbconvert_exporter": "python", 46 | "pygments_lexer": "ipython3", 47 | "version": "3.7.3" 48 | } 49 | }, 50 | "nbformat": 4, 51 | "nbformat_minor": 2 52 | } 53 | -------------------------------------------------------------------------------- /Chapter05/.DS_Store: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/PacktWorkshops/The-Data-Wrangling-Workshop/ddad20f8676602ac6624e72e802769fcaff45b0f/Chapter05/.DS_Store -------------------------------------------------------------------------------- /Chapter05/Exercise 5.13/.ipynb_checkpoints/Exercise 5.13-checkpoint.ipynb: -------------------------------------------------------------------------------- 1 | { 2 | "cells": [ 3 | { 4 | "cell_type": "code", 5 | "execution_count": 3, 6 | "metadata": {}, 7 | "outputs": [], 8 | "source": [ 9 | "import pandas as pd\n", 10 | "from bs4 import BeautifulSoup\n", 11 | "\n", 12 | "d = open(\"../datasets/test.html\", \"r\")\n", 13 | "soup = BeautifulSoup(d)\n", 14 | "lis = soup.find('ul').findAll('li')\n", 15 | "stack = []\n", 16 | "for li in lis:\n", 17 | " a = li.find('a', href=True)\n" 18 | ] 19 | }, 20 | { 21 | "cell_type": "code", 22 | "execution_count": 4, 23 | "metadata": {}, 24 | "outputs": [ 25 | { 26 | "name": "stdout", 27 | "output_type": "stream", 28 | "text": [ 29 | "['https://www.imdb.com/chart/top']\n" 30 | ] 31 | } 32 | ], 33 | "source": [ 34 | "stack.append(a['href'])\n", 35 | "print(stack)" 36 | ] 37 | } 38 | ], 39 | "metadata": { 40 | "kernelspec": { 41 | "display_name": "Python 3", 42 | "language": "python", 43 | "name": "python3" 44 | }, 45 | "language_info": { 46 | "codemirror_mode": { 47 | "name": "ipython", 48 | "version": 3 49 | }, 50 | "file_extension": ".py", 51 | "mimetype": "text/x-python", 52 | "name": "python", 53 | "nbconvert_exporter": "python", 54 | "pygments_lexer": "ipython3", 55 | "version": "3.7.4" 56 | } 57 | }, 58 | "nbformat": 4, 59 | "nbformat_minor": 2 60 | } 61 | -------------------------------------------------------------------------------- /Chapter05/Exercise 5.13/Exercise 5.13.ipynb: -------------------------------------------------------------------------------- 1 | { 2 | "cells": [ 3 | { 4 | "cell_type": "code", 5 | "execution_count": 3, 6 | "metadata": {}, 7 | "outputs": [], 8 | "source": [ 9 | "import pandas as pd\n", 10 | "from bs4 import BeautifulSoup\n", 11 | "\n", 12 | "d = open(\"../datasets/test.html\", \"r\")\n", 13 | "soup = BeautifulSoup(d)\n", 14 | "lis = soup.find('ul').findAll('li')\n", 15 | "stack = []\n", 16 | "for li in lis:\n", 17 | " a = li.find('a', href=True)\n" 18 | ] 19 | }, 20 | { 21 | "cell_type": "code", 22 | "execution_count": 4, 23 | "metadata": {}, 24 | "outputs": [ 25 | { 26 | "name": "stdout", 27 | "output_type": "stream", 28 | "text": [ 29 | "['https://www.imdb.com/chart/top']\n" 30 | ] 31 | } 32 | ], 33 | "source": [ 34 | "stack.append(a['href'])\n", 35 | "print(stack)" 36 | ] 37 | } 38 | ], 39 | "metadata": { 40 | "kernelspec": { 41 | "display_name": "Python 3", 42 | "language": "python", 43 | "name": "python3" 44 | }, 45 | "language_info": { 46 | "codemirror_mode": { 47 | "name": "ipython", 48 | "version": 3 49 | }, 50 | "file_extension": ".py", 51 | "mimetype": "text/x-python", 52 | "name": "python", 53 | "nbconvert_exporter": "python", 54 | "pygments_lexer": "ipython3", 55 | "version": "3.7.4" 56 | } 57 | }, 58 | "nbformat": 4, 59 | "nbformat_minor": 2 60 | } 61 | -------------------------------------------------------------------------------- /Chapter05/datasets/CSV_EX_1.csv: -------------------------------------------------------------------------------- 1 | Bedroom, Sq. foot, Locality, Price ($) 2 | 2, 1500, Good, 300000 3 | 3, 1300, Fair, 240000 4 | 3, 1900, Very good, 450000 5 | 3, 1850, Bad, 280000 6 | 2, 1640, Good, 310000 7 | -------------------------------------------------------------------------------- /Chapter05/datasets/CSV_EX_2.csv: -------------------------------------------------------------------------------- 1 | 2, 1500, Good, 300000 2 | 3, 1300, Fair, 240000 3 | 3, 1900, Very good, 450000 4 | 3, 1850, Bad, 280000 5 | 2, 1640, Good, 310000 6 | -------------------------------------------------------------------------------- /Chapter05/datasets/CSV_EX_3.csv: -------------------------------------------------------------------------------- 1 | Bedroom; Sq. foot; Locality; Price ($) 2 | 2; 1500; Good; 300000 3 | 3; 1300; Fair; 240000 4 | 3; 1900; Very good; 450000 5 | 3; 1850; Bad; 280000 6 | 2; 1640; Good; 310000 -------------------------------------------------------------------------------- /Chapter05/datasets/CSV_EX_blankline.csv: -------------------------------------------------------------------------------- 1 | Bedroom, Sq. foot, Locality, Price ($) 2 | 2, 1500, Good, 300000 3 | 3, 1300, Fair, 240000 4 | 5 | 3, 1900, Very good, 450000 6 | 3, 1850, Bad, 280000 7 | 8 | 2, 1640, Good, 310000 9 | -------------------------------------------------------------------------------- /Chapter05/datasets/CSV_EX_skipfooter.csv: -------------------------------------------------------------------------------- 1 | Filetype: CSV,,, 2 | ,Info about some houses,, 3 | Bedroom, Sq. foot, Locality, Price ($) 4 | 2,1500, Good,300000 5 | 3,1300, Fair,240000 6 | 3,1900, Very good,450000 7 | 3,1850, Bad,280000 8 | 2,1640, Good,310000 9 | , This is the end of file,, -------------------------------------------------------------------------------- /Chapter05/datasets/CSV_EX_skiprows.csv: -------------------------------------------------------------------------------- 1 | Filetype: CSV,,, 2 | ,Info about some houses,, 3 | Bedroom, Sq. foot, Locality, Price ($) 4 | 2,1500, Good,300000 5 | 3,1300, Fair,240000 6 | 3,1900, Very good,450000 7 | 3,1850, Bad,280000 8 | 2,1640, Good,310000 9 | 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/Chapter05/datasets/Table_tab_separated.txt: -------------------------------------------------------------------------------- 1 | Bedroom Sq. foot Locality Price ($) 2 | 2 1500 Good 300000 3 | 3 1300 Fair 240000 4 | 3 1900 Very good 450000 5 | 3 1850 Bad 280000 6 | 2 1640 Good 310000 7 | -------------------------------------------------------------------------------- /Chapter05/datasets/test_output.xlsx: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/PacktWorkshops/The-Data-Wrangling-Workshop/ddad20f8676602ac6624e72e802769fcaff45b0f/Chapter05/datasets/test_output.xlsx -------------------------------------------------------------------------------- /Chapter05/unit_tests/.ipynb_checkpoints/Exercise 61-checkpoint.ipynb: -------------------------------------------------------------------------------- 1 | { 2 | "cells": [ 3 | { 4 | "cell_type": "code", 5 | "execution_count": null, 6 | "metadata": {}, 7 | "outputs": [], 8 | "source": [ 9 | "def test_exercise_61_1(x) -> bool:\n", 10 | " import pandas as pd \n", 11 | " df = pd.read_csv(\"../CSV_EX_3.csv\")\n", 12 | " return df.equals(x)" 13 | ] 14 | }, 15 | { 16 | "cell_type": "code", 17 | "execution_count": null, 18 | "metadata": {}, 19 | "outputs": [], 20 | "source": [ 21 | "def test_exercise_61_2(x) -> bool:\n", 22 | " import pandas as pd \n", 23 | " df = pd.read_csv(\"../CSV_EX_3.csv\",sep=';')\n", 24 | " return df.equals(x)" 25 | ] 26 | }, 27 | { 28 | "cell_type": "code", 29 | "execution_count": null, 30 | "metadata": {}, 31 | "outputs": [], 32 | "source": [] 33 | } 34 | ], 35 | "metadata": { 36 | "kernelspec": { 37 | "display_name": "Python 3", 38 | "language": "python", 39 | "name": "python3" 40 | }, 41 | "language_info": { 42 | "codemirror_mode": { 43 | "name": "ipython", 44 | "version": 3 45 | }, 46 | "file_extension": ".py", 47 | "mimetype": "text/x-python", 48 | "name": "python", 49 | "nbconvert_exporter": "python", 50 | "pygments_lexer": "ipython3", 51 | "version": "3.7.3" 52 | } 53 | }, 54 | "nbformat": 4, 55 | "nbformat_minor": 2 56 | } 57 | -------------------------------------------------------------------------------- /Chapter05/unit_tests/.ipynb_checkpoints/Exercise 62-checkpoint.ipynb: -------------------------------------------------------------------------------- 1 | { 2 | "cells": [ 3 | { 4 | "cell_type": "code", 5 | "execution_count": null, 6 | "metadata": {}, 7 | "outputs": [], 8 | "source": [ 9 | "def test_exercise_62_1(x) -> bool:\n", 10 | " import pandas as pd \n", 11 | " df = pd.read_csv(\"../CSV_EX_1.csv\",names=['A','B','C','D']) \n", 12 | " return df.equals(x)" 13 | ] 14 | }, 15 | { 16 | "cell_type": "code", 17 | "execution_count": null, 18 | "metadata": {}, 19 | "outputs": [], 20 | "source": [ 21 | "def test_exercise_62_2(x) -> bool:\n", 22 | " import pandas as pd \n", 23 | " df = pd.read_csv(\"../CSV_EX_1.csv\",header=0,names=['A','B','C','D']) \n", 24 | " return df.equals(x)" 25 | ] 26 | } 27 | ], 28 | "metadata": { 29 | "kernelspec": { 30 | "display_name": "Python 3", 31 | "language": "python", 32 | "name": "python3" 33 | }, 34 | "language_info": { 35 | "codemirror_mode": { 36 | "name": "ipython", 37 | "version": 3 38 | }, 39 | "file_extension": ".py", 40 | "mimetype": "text/x-python", 41 | "name": "python", 42 | "nbconvert_exporter": "python", 43 | "pygments_lexer": "ipython3", 44 | "version": "3.7.3" 45 | } 46 | }, 47 | "nbformat": 4, 48 | "nbformat_minor": 2 49 | } 50 | -------------------------------------------------------------------------------- /Chapter05/unit_tests/.ipynb_checkpoints/Exercise 64-checkpoint.ipynb: -------------------------------------------------------------------------------- 1 | { 2 | "cells": [ 3 | { 4 | "cell_type": "code", 5 | "execution_count": null, 6 | "metadata": {}, 7 | "outputs": [], 8 | "source": [ 9 | "def test_exercise_64_1(x) -> bool:\n", 10 | " import pandas as pd \n", 11 | " l = list_of_dataframe = [] \n", 12 | " return l == x" 13 | ] 14 | }, 15 | { 16 | "cell_type": "code", 17 | "execution_count": null, 18 | "metadata": {}, 19 | "outputs": [], 20 | "source": [ 21 | "def test_exercise_64_2(x) -> bool:\n", 22 | " import pandas as pd \n", 23 | " rows_in_a_chunk = 10 \n", 24 | " return rows_in_a_chunk == x" 25 | ] 26 | }, 27 | { 28 | "cell_type": "code", 29 | "execution_count": null, 30 | "metadata": {}, 31 | "outputs": [], 32 | "source": [ 33 | "def test_exercise_64_3(x) -> bool:\n", 34 | " import pandas as pd \n", 35 | " num_chunks = 5 \n", 36 | " return num_chunks == x" 37 | ] 38 | } 39 | ], 40 | "metadata": { 41 | "kernelspec": { 42 | "display_name": "Python 3", 43 | "language": "python", 44 | "name": "python3" 45 | }, 46 | "language_info": { 47 | "codemirror_mode": { 48 | "name": "ipython", 49 | "version": 3 50 | }, 51 | "file_extension": ".py", 52 | "mimetype": "text/x-python", 53 | "name": "python", 54 | "nbconvert_exporter": "python", 55 | "pygments_lexer": "ipython3", 56 | "version": "3.7.3" 57 | } 58 | }, 59 | "nbformat": 4, 60 | "nbformat_minor": 2 61 | } 62 | -------------------------------------------------------------------------------- /Chapter05/unit_tests/.ipynb_checkpoints/Exercise 65-checkpoint.ipynb: -------------------------------------------------------------------------------- 1 | { 2 | "cells": [ 3 | { 4 | "cell_type": "code", 5 | "execution_count": null, 6 | "metadata": {}, 7 | "outputs": [], 8 | "source": [ 9 | "def test_exercise_65_1(x) -> bool:\n", 10 | " import pandas as pd \n", 11 | " df = pd.read_table(\"../Table_EX_1.txt\") \n", 12 | " return df.equals(x)" 13 | ] 14 | }, 15 | { 16 | "cell_type": "code", 17 | "execution_count": null, 18 | "metadata": {}, 19 | "outputs": [], 20 | "source": [ 21 | "def test_exercise_65_2(x) -> bool:\n", 22 | " import pandas as pd \n", 23 | " df = pd.read_table(\"../Table_EX_1.txt\",sep=',') \n", 24 | " return df.equals(x)" 25 | ] 26 | } 27 | ], 28 | "metadata": { 29 | "kernelspec": { 30 | "display_name": "Python 3", 31 | "language": "python", 32 | "name": "python3" 33 | }, 34 | "language_info": { 35 | "codemirror_mode": { 36 | "name": "ipython", 37 | "version": 3 38 | }, 39 | "file_extension": ".py", 40 | "mimetype": "text/x-python", 41 | "name": "python", 42 | "nbconvert_exporter": "python", 43 | "pygments_lexer": "ipython3", 44 | "version": "3.7.3" 45 | } 46 | }, 47 | "nbformat": 4, 48 | "nbformat_minor": 2 49 | } 50 | -------------------------------------------------------------------------------- /Chapter05/unit_tests/.ipynb_checkpoints/Exercise 67-checkpoint.ipynb: -------------------------------------------------------------------------------- 1 | { 2 | "cells": [ 3 | { 4 | "cell_type": "code", 5 | "execution_count": null, 6 | "metadata": {}, 7 | "outputs": [], 8 | "source": [ 9 | "def test_exercise_67_1(x) -> bool:\n", 10 | " import pandas as pd \n", 11 | " df = pd.read_json(\"../movies.json\")\n", 12 | " return df.equals(x)" 13 | ] 14 | }, 15 | { 16 | "cell_type": "code", 17 | "execution_count": null, 18 | "metadata": {}, 19 | "outputs": [], 20 | "source": [ 21 | "def test_exercise_67_2(x) -> bool:\n", 22 | " import pandas as pd \n", 23 | " df16 = pd.read_json(\"../movies.json\")\n", 24 | " cast_of_avengers=df16[(df16['title']==\"The Avengers\") & (df16['year']==2012)]['cast'] \n", 25 | " return cast_of_avengers == x" 26 | ] 27 | } 28 | ], 29 | "metadata": { 30 | "kernelspec": { 31 | "display_name": "Python 3", 32 | "language": "python", 33 | "name": "python3" 34 | }, 35 | "language_info": { 36 | "codemirror_mode": { 37 | "name": "ipython", 38 | "version": 3 39 | }, 40 | "file_extension": ".py", 41 | "mimetype": "text/x-python", 42 | "name": "python", 43 | "nbconvert_exporter": "python", 44 | "pygments_lexer": "ipython3", 45 | "version": "3.7.3" 46 | } 47 | }, 48 | "nbformat": 4, 49 | "nbformat_minor": 2 50 | } 51 | -------------------------------------------------------------------------------- /Chapter05/unit_tests/Exercise 61.ipynb: -------------------------------------------------------------------------------- 1 | { 2 | "cells": [ 3 | { 4 | "cell_type": "code", 5 | "execution_count": null, 6 | "metadata": {}, 7 | "outputs": [], 8 | "source": [ 9 | "def test_exercise_61_1(x) -> bool:\n", 10 | " import pandas as pd \n", 11 | " df = pd.read_csv(\"../CSV_EX_3.csv\")\n", 12 | " return df.equals(x)" 13 | ] 14 | }, 15 | { 16 | "cell_type": "code", 17 | "execution_count": null, 18 | "metadata": {}, 19 | "outputs": [], 20 | "source": [ 21 | "def test_exercise_61_2(x) -> bool:\n", 22 | " import pandas as pd \n", 23 | " df = pd.read_csv(\"../CSV_EX_3.csv\",sep=';')\n", 24 | " return df.equals(x)" 25 | ] 26 | }, 27 | { 28 | "cell_type": "code", 29 | "execution_count": null, 30 | "metadata": {}, 31 | "outputs": [], 32 | "source": [] 33 | } 34 | ], 35 | "metadata": { 36 | "kernelspec": { 37 | "display_name": "Python 3", 38 | "language": "python", 39 | "name": "python3" 40 | }, 41 | "language_info": { 42 | "codemirror_mode": { 43 | "name": "ipython", 44 | "version": 3 45 | }, 46 | "file_extension": ".py", 47 | "mimetype": "text/x-python", 48 | "name": "python", 49 | "nbconvert_exporter": "python", 50 | "pygments_lexer": "ipython3", 51 | "version": "3.7.3" 52 | } 53 | }, 54 | "nbformat": 4, 55 | "nbformat_minor": 2 56 | } 57 | -------------------------------------------------------------------------------- /Chapter05/unit_tests/Exercise 62.ipynb: -------------------------------------------------------------------------------- 1 | { 2 | "cells": [ 3 | { 4 | "cell_type": "code", 5 | "execution_count": null, 6 | "metadata": {}, 7 | "outputs": [], 8 | "source": [ 9 | "def test_exercise_62_1(x) -> bool:\n", 10 | " import pandas as pd \n", 11 | " df = pd.read_csv(\"../CSV_EX_1.csv\",names=['A','B','C','D']) \n", 12 | " return df.equals(x)" 13 | ] 14 | }, 15 | { 16 | "cell_type": "code", 17 | "execution_count": null, 18 | "metadata": {}, 19 | "outputs": [], 20 | "source": [ 21 | "def test_exercise_62_2(x) -> bool:\n", 22 | " import pandas as pd \n", 23 | " df = pd.read_csv(\"../CSV_EX_1.csv\",header=0,names=['A','B','C','D']) \n", 24 | " return df.equals(x)" 25 | ] 26 | } 27 | ], 28 | "metadata": { 29 | "kernelspec": { 30 | "display_name": "Python 3", 31 | "language": "python", 32 | "name": "python3" 33 | }, 34 | "language_info": { 35 | "codemirror_mode": { 36 | "name": "ipython", 37 | "version": 3 38 | }, 39 | "file_extension": ".py", 40 | "mimetype": "text/x-python", 41 | "name": "python", 42 | "nbconvert_exporter": "python", 43 | "pygments_lexer": "ipython3", 44 | "version": "3.7.3" 45 | } 46 | }, 47 | "nbformat": 4, 48 | "nbformat_minor": 2 49 | } 50 | -------------------------------------------------------------------------------- /Chapter05/unit_tests/Exercise 65.ipynb: -------------------------------------------------------------------------------- 1 | { 2 | "cells": [ 3 | { 4 | "cell_type": "code", 5 | "execution_count": null, 6 | "metadata": {}, 7 | "outputs": [], 8 | "source": [ 9 | "def test_exercise_65_1(x) -> bool:\n", 10 | " import pandas as pd \n", 11 | " df = pd.read_table(\"../Table_EX_1.txt\") \n", 12 | " return df.equals(x)" 13 | ] 14 | }, 15 | { 16 | "cell_type": "code", 17 | "execution_count": null, 18 | "metadata": {}, 19 | "outputs": [], 20 | "source": [ 21 | "def test_exercise_65_2(x) -> bool:\n", 22 | " import pandas as pd \n", 23 | " df = pd.read_table(\"../Table_EX_1.txt\",sep=',') \n", 24 | " return df.equals(x)" 25 | ] 26 | } 27 | ], 28 | "metadata": { 29 | "kernelspec": { 30 | "display_name": "Python 3", 31 | "language": "python", 32 | "name": "python3" 33 | }, 34 | "language_info": { 35 | "codemirror_mode": { 36 | "name": "ipython", 37 | "version": 3 38 | }, 39 | "file_extension": ".py", 40 | "mimetype": "text/x-python", 41 | "name": "python", 42 | "nbconvert_exporter": "python", 43 | "pygments_lexer": "ipython3", 44 | "version": "3.7.3" 45 | } 46 | }, 47 | "nbformat": 4, 48 | "nbformat_minor": 2 49 | } 50 | -------------------------------------------------------------------------------- /Chapter05/unit_tests/Exercise 67.ipynb: -------------------------------------------------------------------------------- 1 | { 2 | "cells": [ 3 | { 4 | "cell_type": "code", 5 | "execution_count": null, 6 | "metadata": {}, 7 | "outputs": [], 8 | "source": [ 9 | "def test_exercise_67_1(x) -> bool:\n", 10 | " import pandas as pd \n", 11 | " df = pd.read_json(\"../movies.json\")\n", 12 | " return df.equals(x)" 13 | ] 14 | }, 15 | { 16 | "cell_type": "code", 17 | "execution_count": null, 18 | "metadata": {}, 19 | "outputs": [], 20 | "source": [ 21 | "def test_exercise_67_2(x) -> bool:\n", 22 | " import pandas as pd \n", 23 | " df16 = pd.read_json(\"../movies.json\")\n", 24 | " cast_of_avengers=df16[(df16['title']==\"The Avengers\") & (df16['year']==2012)]['cast'] \n", 25 | " return cast_of_avengers == x" 26 | ] 27 | } 28 | ], 29 | "metadata": { 30 | "kernelspec": { 31 | "display_name": "Python 3", 32 | "language": "python", 33 | "name": "python3" 34 | }, 35 | "language_info": { 36 | "codemirror_mode": { 37 | "name": "ipython", 38 | "version": 3 39 | }, 40 | "file_extension": ".py", 41 | "mimetype": "text/x-python", 42 | "name": "python", 43 | "nbconvert_exporter": "python", 44 | "pygments_lexer": "ipython3", 45 | "version": "3.7.3" 46 | } 47 | }, 48 | "nbformat": 4, 49 | "nbformat_minor": 2 50 | } 51 | -------------------------------------------------------------------------------- /Chapter06/.ipynb_checkpoints/Exercise 01-checkpoint.ipynb: -------------------------------------------------------------------------------- 1 | { 2 | "cells": [ 3 | { 4 | "cell_type": "code", 5 | "execution_count": null, 6 | "metadata": {}, 7 | "outputs": [], 8 | "source": [ 9 | "def test_exercise_1_1(x) -> bool:\n", 10 | " return [x for x in range(100000) if x % 2 != 0] == x" 11 | ] 12 | }, 13 | { 14 | "cell_type": "code", 15 | "execution_count": null, 16 | "metadata": {}, 17 | "outputs": [], 18 | "source": [ 19 | "def test_exercise_1_2(x) -> bool:\n", 20 | " from sys import getsizeof\n", 21 | " odd_numbers2 = [x for x in range(100000) if x % 2 != 0]\n", 22 | " return getsizeof(odd_numbers2) == x" 23 | ] 24 | }, 25 | { 26 | "cell_type": "code", 27 | "execution_count": null, 28 | "metadata": {}, 29 | "outputs": [], 30 | "source": [ 31 | "def test_exercise_1_3(x) -> bool:\n", 32 | " from sys import getsizeof\n", 33 | " odd_numbers = (x for x in range(100000) if x % 2 != 0)\n", 34 | " return getsizeof(odd_numbers2) == x" 35 | ] 36 | } 37 | ], 38 | "metadata": { 39 | "kernelspec": { 40 | "display_name": "Python 3", 41 | "language": "python", 42 | "name": "python3" 43 | }, 44 | "language_info": { 45 | "codemirror_mode": { 46 | "name": "ipython", 47 | "version": 3 48 | }, 49 | "file_extension": ".py", 50 | "mimetype": "text/x-python", 51 | "name": "python", 52 | "nbconvert_exporter": "python", 53 | "pygments_lexer": "ipython3", 54 | "version": "3.7.3" 55 | } 56 | }, 57 | "nbformat": 4, 58 | "nbformat_minor": 2 59 | } 60 | -------------------------------------------------------------------------------- /Chapter06/.ipynb_checkpoints/Exercise 6.02-checkpoint.ipynb: -------------------------------------------------------------------------------- 1 | { 2 | "cells": [], 3 | "metadata": {}, 4 | "nbformat": 4, 5 | "nbformat_minor": 2 6 | } 7 | -------------------------------------------------------------------------------- /Chapter06/.ipynb_checkpoints/Exercise 6.06-checkpoint.ipynb: -------------------------------------------------------------------------------- 1 | { 2 | "cells": [], 3 | "metadata": {}, 4 | "nbformat": 4, 5 | "nbformat_minor": 2 6 | } 7 | -------------------------------------------------------------------------------- /Chapter06/.ipynb_checkpoints/Exercise 6.09-checkpoint.ipynb: -------------------------------------------------------------------------------- 1 | { 2 | "cells": [ 3 | { 4 | "cell_type": "code", 5 | "execution_count": 1, 6 | "metadata": {}, 7 | "outputs": [], 8 | "source": [ 9 | "ship_data = {\"Sea Princess\": {\"date\":\"12/08/20\", \"load\": 40000}, \n", 10 | " \"Sea Pincess\": {\"date\":\"10/06/20\", \"load\": 30000}, \n", 11 | " \"Sea Princes\": {\"date\":\"12/04/20\", \"load\": 30000}}\n" 12 | ] 13 | }, 14 | { 15 | "cell_type": "code", 16 | "execution_count": null, 17 | "metadata": {}, 18 | "outputs": [], 19 | "source": [ 20 | "from Levenshtein import distance\n", 21 | "name_of_ship = \"Sea Princess\"\n", 22 | "for k, v in ship_data.items():\n", 23 | " print(\"{} {} {}\".format(k, name_of_ship, distance(name_of_ship, k)))\n" 24 | ] 25 | } 26 | ], 27 | "metadata": { 28 | "kernelspec": { 29 | "display_name": "Python 3", 30 | "language": "python", 31 | "name": "python3" 32 | }, 33 | "language_info": { 34 | "codemirror_mode": { 35 | "name": "ipython", 36 | "version": 3 37 | }, 38 | "file_extension": ".py", 39 | "mimetype": "text/x-python", 40 | "name": "python", 41 | "nbconvert_exporter": "python", 42 | "pygments_lexer": "ipython3", 43 | "version": "3.7.3" 44 | } 45 | }, 46 | "nbformat": 4, 47 | "nbformat_minor": 2 48 | } 49 | -------------------------------------------------------------------------------- /Chapter06/.ipynb_checkpoints/Exercise 8-checkpoint.ipynb: -------------------------------------------------------------------------------- 1 | { 2 | "cells": [ 3 | { 4 | "cell_type": "code", 5 | "execution_count": null, 6 | "metadata": {}, 7 | "outputs": [], 8 | "source": [ 9 | "def test_exercise_8_1(x) -> bool:\n", 10 | " ship_data = {\"Sea Princess\": {\"date\":\"12/08/20\", \"load\": 40000},  \n", 11 | " \"Sea Princess\": {\"date\":\"10/06/20\", \"load\": 30000},  \n", 12 | " \"Sea Princess\": {\"date\":\"12/04/20\", \"load\": 30000}} \n", 13 | " return df_original == x" 14 | ] 15 | }, 16 | { 17 | "cell_type": "code", 18 | "execution_count": null, 19 | "metadata": {}, 20 | "outputs": [], 21 | "source": [] 22 | } 23 | ], 24 | "metadata": { 25 | "kernelspec": { 26 | "display_name": "Python 3", 27 | "language": "python", 28 | "name": "python3" 29 | }, 30 | "language_info": { 31 | "codemirror_mode": { 32 | "name": "ipython", 33 | "version": 3 34 | }, 35 | "file_extension": ".py", 36 | "mimetype": "text/x-python", 37 | "name": "python", 38 | "nbconvert_exporter": "python", 39 | "pygments_lexer": "ipython3", 40 | "version": "3.7.3" 41 | } 42 | }, 43 | "nbformat": 4, 44 | "nbformat_minor": 2 45 | } 46 | -------------------------------------------------------------------------------- /Chapter06/datasets/combinded_data.csv: -------------------------------------------------------------------------------- 1 | Name,Age,Height,Weight,Disease_history,Heart_problem 2 | Bob,23.0,1.7,70,N,N 3 | Alex,45,1.61,61,Y,N 4 | George,12.5,1.4,40,N, 5 | Alice,34,1.56,51,N,Y -------------------------------------------------------------------------------- /Chapter06/datasets/dummy_data.csv: -------------------------------------------------------------------------------- 1 | Bob, 23, 1.7, 70, N, N 2 | Alex, 45, 1.61, 61, Y, N 3 | George, 12, 1.4, 40, N, 4 | Alice, 34, 1.56, 51, N, Y -------------------------------------------------------------------------------- /Chapter06/datasets/dummy_header.csv: -------------------------------------------------------------------------------- 1 | Name 2 | Age 3 | Height 4 | Weight 5 | Family Sickness History 6 | Suffering from Heart Problem -------------------------------------------------------------------------------- /Chapter06/unit_testing/Exercise 6.01.ipynb: -------------------------------------------------------------------------------- 1 | { 2 | "cells": [ 3 | { 4 | "cell_type": "code", 5 | "execution_count": null, 6 | "metadata": {}, 7 | "outputs": [], 8 | "source": [ 9 | "def test_exercise_1_1(x) -> bool:\n", 10 | " return [x for x in range(100000) if x % 2 != 0] == x" 11 | ] 12 | }, 13 | { 14 | "cell_type": "code", 15 | "execution_count": null, 16 | "metadata": {}, 17 | "outputs": [], 18 | "source": [ 19 | "def test_exercise_1_2(x) -> bool:\n", 20 | " from sys import getsizeof\n", 21 | " odd_numbers2 = [x for x in range(100000) if x % 2 != 0]\n", 22 | " return getsizeof(odd_numbers2) == x" 23 | ] 24 | }, 25 | { 26 | "cell_type": "code", 27 | "execution_count": null, 28 | "metadata": {}, 29 | "outputs": [], 30 | "source": [ 31 | "def test_exercise_1_3(x) -> bool:\n", 32 | " from sys import getsizeof\n", 33 | " odd_numbers = (x for x in range(100000) if x % 2 != 0)\n", 34 | " return getsizeof(odd_numbers2) == x" 35 | ] 36 | } 37 | ], 38 | "metadata": { 39 | "kernelspec": { 40 | "display_name": "Python 3", 41 | "language": "python", 42 | "name": "python3" 43 | }, 44 | "language_info": { 45 | "codemirror_mode": { 46 | "name": "ipython", 47 | "version": 3 48 | }, 49 | "file_extension": ".py", 50 | "mimetype": "text/x-python", 51 | "name": "python", 52 | "nbconvert_exporter": "python", 53 | "pygments_lexer": "ipython3", 54 | "version": "3.7.3" 55 | } 56 | }, 57 | "nbformat": 4, 58 | "nbformat_minor": 2 59 | } 60 | -------------------------------------------------------------------------------- /Chapter06/unit_testing/Exercise 6.07.ipynb: -------------------------------------------------------------------------------- 1 | { 2 | "cells": [ 3 | { 4 | "cell_type": "code", 5 | "execution_count": null, 6 | "metadata": {}, 7 | "outputs": [], 8 | "source": [ 9 | "def test_exercise_7_1(x) -> bool:\n", 10 | " import pandas as pd \n", 11 | " ys = [cos(i*(pi/4)) for i in range(50)] \n", 12 | " ys[4] = ys[4] + 5.0 \n", 13 | " ys[20] = ys[20] + 8.0 \n", 14 | " df_original = pd.DataFrame(ys) \n", 15 | " return df_original == x" 16 | ] 17 | }, 18 | { 19 | "cell_type": "code", 20 | "execution_count": null, 21 | "metadata": {}, 22 | "outputs": [], 23 | "source": [ 24 | "def test_exercise_7_2(x) -> bool:\n", 25 | " import pandas as pd \n", 26 | " ys = [cos(i*(pi/4)) for i in range(50)] \n", 27 | " ys[4] = ys[4] + 5.0 \n", 28 | " ys[20] = ys[20] + 8.0 \n", 29 | " df_original = pd.DataFrame(ys) \n", 30 | " cos_arr_without_outliers = df_original[(cos_arr_z_score < 3)]\n", 31 | " return cos_arr_without_outliers == x" 32 | ] 33 | } 34 | ], 35 | "metadata": { 36 | "kernelspec": { 37 | "display_name": "Python 3", 38 | "language": "python", 39 | "name": "python3" 40 | }, 41 | "language_info": { 42 | "codemirror_mode": { 43 | "name": "ipython", 44 | "version": 3 45 | }, 46 | "file_extension": ".py", 47 | "mimetype": "text/x-python", 48 | "name": "python", 49 | "nbconvert_exporter": "python", 50 | "pygments_lexer": "ipython3", 51 | "version": "3.7.3" 52 | } 53 | }, 54 | "nbformat": 4, 55 | "nbformat_minor": 2 56 | } 57 | -------------------------------------------------------------------------------- /Chapter06/unit_testing/Exercise 6.08.ipynb: -------------------------------------------------------------------------------- 1 | { 2 | "cells": [ 3 | { 4 | "cell_type": "code", 5 | "execution_count": 8, 6 | "metadata": {}, 7 | "outputs": [], 8 | "source": [ 9 | "def test_exercise_8_1(x) -> bool:\n", 10 | " ship_data = {\"Sea Princess\": {\"date\":\"12/08/20\", \"load\": 40000},\n", 11 | " \"Sea Princess\": {\"date\":\"10/06/20\", \"load\": 30000},\n", 12 | " \"Sea Princess\": {\"date\":\"12/04/20\", \"load\": 30000}} \n", 13 | " return df_original == x" 14 | ] 15 | } 16 | ], 17 | "metadata": { 18 | "kernelspec": { 19 | "display_name": "Python 3", 20 | "language": "python", 21 | "name": "python3" 22 | }, 23 | "language_info": { 24 | "codemirror_mode": { 25 | "name": "ipython", 26 | "version": 3 27 | }, 28 | "file_extension": ".py", 29 | "mimetype": "text/x-python", 30 | "name": "python", 31 | "nbconvert_exporter": "python", 32 | "pygments_lexer": "ipython3", 33 | "version": "3.7.3" 34 | } 35 | }, 36 | "nbformat": 4, 37 | "nbformat_minor": 2 38 | } 39 | -------------------------------------------------------------------------------- /Chapter07/.ipynb_checkpoints/Exercise 7.01-checkpoint.ipynb: -------------------------------------------------------------------------------- 1 | { 2 | "cells": [ 3 | { 4 | "cell_type": "code", 5 | "execution_count": null, 6 | "metadata": {}, 7 | "outputs": [], 8 | "source": [ 9 | "import requests " 10 | ] 11 | }, 12 | { 13 | "cell_type": "code", 14 | "execution_count": null, 15 | "metadata": {}, 16 | "outputs": [], 17 | "source": [ 18 | "# First assign the URL of Wikipedia home page to a strings \n", 19 | "\n", 20 | "wiki_home = \"https://en.wikipedia.org/wiki/Main_Page\" " 21 | ] 22 | }, 23 | { 24 | "cell_type": "code", 25 | "execution_count": null, 26 | "metadata": {}, 27 | "outputs": [], 28 | "source": [ 29 | "response = requests.get(wiki_home) \n", 30 | "\n", 31 | "response " 32 | ] 33 | }, 34 | { 35 | "cell_type": "code", 36 | "execution_count": null, 37 | "metadata": {}, 38 | "outputs": [], 39 | "source": [ 40 | "type(response) " 41 | ] 42 | } 43 | ], 44 | "metadata": { 45 | "kernelspec": { 46 | "display_name": "Python 3", 47 | "language": "python", 48 | "name": "python3" 49 | }, 50 | "language_info": { 51 | "codemirror_mode": { 52 | "name": "ipython", 53 | "version": 3 54 | }, 55 | "file_extension": ".py", 56 | "mimetype": "text/x-python", 57 | "name": "python", 58 | "nbconvert_exporter": "python", 59 | "pygments_lexer": "ipython3", 60 | "version": "3.7.3" 61 | } 62 | }, 63 | "nbformat": 4, 64 | "nbformat_minor": 2 65 | } 66 | -------------------------------------------------------------------------------- /Chapter07/.ipynb_checkpoints/Exercise 7.07-checkpoint.ipynb: -------------------------------------------------------------------------------- 1 | { 2 | "cells": [ 3 | { 4 | "cell_type": "code", 5 | "execution_count": null, 6 | "metadata": {}, 7 | "outputs": [], 8 | "source": [ 9 | "data = '''\n", 10 | "\n", 11 | " Dave\n", 12 | " Piccardo\n", 13 | " \n", 14 | " +1 742 101 4456\n", 15 | " \n", 16 | " \n", 17 | " dave.p@gmail.com\n", 18 | "'''\n" 19 | ] 20 | }, 21 | { 22 | "cell_type": "code", 23 | "execution_count": null, 24 | "metadata": {}, 25 | "outputs": [], 26 | "source": [ 27 | "import xml.etree.ElementTree as ET\n", 28 | "tree = ET.fromstring(data)\n", 29 | "type (tree)\n" 30 | ] 31 | }, 32 | { 33 | "cell_type": "code", 34 | "execution_count": null, 35 | "metadata": {}, 36 | "outputs": [], 37 | "source": [] 38 | } 39 | ], 40 | "metadata": { 41 | "kernelspec": { 42 | "display_name": "Python 3", 43 | "language": "python", 44 | "name": "python3" 45 | }, 46 | "language_info": { 47 | "codemirror_mode": { 48 | "name": "ipython", 49 | "version": 3 50 | }, 51 | "file_extension": ".py", 52 | "mimetype": "text/x-python", 53 | "name": "python", 54 | "nbconvert_exporter": "python", 55 | "pygments_lexer": "ipython3", 56 | "version": "3.7.3" 57 | } 58 | }, 59 | "nbformat": 4, 60 | "nbformat_minor": 2 61 | } 62 | -------------------------------------------------------------------------------- /Chapter07/.ipynb_checkpoints/Exercise 7.08-checkpoint.ipynb: -------------------------------------------------------------------------------- 1 | { 2 | "cells": [], 3 | "metadata": {}, 4 | "nbformat": 4, 5 | "nbformat_minor": 2 6 | } 7 | -------------------------------------------------------------------------------- /Chapter07/.ipynb_checkpoints/Exercise 7.12-checkpoint.ipynb: -------------------------------------------------------------------------------- 1 | { 2 | "cells": [], 3 | "metadata": {}, 4 | "nbformat": 4, 5 | "nbformat_minor": 2 6 | } 7 | -------------------------------------------------------------------------------- /Chapter07/.ipynb_checkpoints/Exercise 7.17-checkpoint.ipynb: -------------------------------------------------------------------------------- 1 | { 2 | "cells": [], 3 | "metadata": {}, 4 | "nbformat": 4, 5 | "nbformat_minor": 2 6 | } 7 | -------------------------------------------------------------------------------- /Chapter07/.ipynb_checkpoints/Untitled-checkpoint.ipynb: -------------------------------------------------------------------------------- 1 | { 2 | "cells": [], 3 | "metadata": {}, 4 | "nbformat": 4, 5 | "nbformat_minor": 2 6 | } 7 | -------------------------------------------------------------------------------- /Chapter07/Activity 7.02/APIkeys.json: -------------------------------------------------------------------------------- 1 | { 2 | "OMDBapi": "enterapikeyhere" 3 | } 4 | -------------------------------------------------------------------------------- /Chapter07/Activity 7.02/Posters/Titanic.jpg: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/PacktWorkshops/The-Data-Wrangling-Workshop/ddad20f8676602ac6624e72e802769fcaff45b0f/Chapter07/Activity 7.02/Posters/Titanic.jpg -------------------------------------------------------------------------------- /Chapter07/datasets/xml1.xml: -------------------------------------------------------------------------------- 1 | 2 | 3 | 4 | 1 5 | 2008 6 | 141100 7 | 8 | 9 | 10 | 11 | 4 12 | 2011 13 | 59900 14 | 15 | 16 | 17 | 68 18 | 2011 19 | 13600 20 | 21 | 22 | 23 | -------------------------------------------------------------------------------- /Chapter07/unit_tests/.ipynb_checkpoints/Exercise 87-checkpoint.ipynb: -------------------------------------------------------------------------------- 1 | { 2 | "cells": [ 3 | { 4 | "cell_type": "code", 5 | "execution_count": null, 6 | "metadata": {}, 7 | "outputs": [], 8 | "source": [ 9 | "def test_exercise_87_1(x) -> bool:\n", 10 | " data = \"\"\"\n", 11 | " \n", 12 | " Dave \n", 13 | " Piccardo \n", 14 | " \n", 15 | " +1 742 101 4456 \n", 16 | " \n", 17 | " \n", 18 | " dave.p@gmail.com \n", 19 | "\"\"\" \n", 20 | " return data == x" 21 | ] 22 | }, 23 | { 24 | "cell_type": "code", 25 | "execution_count": null, 26 | "metadata": {}, 27 | "outputs": [], 28 | "source": [ 29 | "def test_exercise_87_2(x) -> bool:\n", 30 | " return \"ml.etree.ElementTree.Element\" == x" 31 | ] 32 | } 33 | ], 34 | "metadata": { 35 | "kernelspec": { 36 | "display_name": "Python 3", 37 | "language": "python", 38 | "name": "python3" 39 | }, 40 | "language_info": { 41 | "codemirror_mode": { 42 | "name": "ipython", 43 | "version": 3 44 | }, 45 | "file_extension": ".py", 46 | "mimetype": "text/x-python", 47 | "name": "python", 48 | "nbconvert_exporter": "python", 49 | "pygments_lexer": "ipython3", 50 | "version": "3.7.3" 51 | } 52 | }, 53 | "nbformat": 4, 54 | "nbformat_minor": 2 55 | } 56 | -------------------------------------------------------------------------------- /Chapter07/unit_tests/.ipynb_checkpoints/Exercise 93-checkpoint.ipynb: -------------------------------------------------------------------------------- 1 | { 2 | "cells": [ 3 | { 4 | "cell_type": "code", 5 | "execution_count": null, 6 | "metadata": {}, 7 | "outputs": [], 8 | "source": [ 9 | "def test_exercise_93_1(x) -> bool:\n", 10 | " import requests, urllib.parse\n", 11 | " serviceurl = 'https://restcountries.eu/rest/v2/name/'\n", 12 | " url = serviceurl + country_name \n", 13 | " uh = urllib.request.urlopen(url) \n", 14 | " return uh == x" 15 | ] 16 | } 17 | ], 18 | "metadata": { 19 | "kernelspec": { 20 | "display_name": "Python 3", 21 | "language": "python", 22 | "name": "python3" 23 | }, 24 | "language_info": { 25 | "codemirror_mode": { 26 | "name": "ipython", 27 | "version": 3 28 | }, 29 | "file_extension": ".py", 30 | "mimetype": "text/x-python", 31 | "name": "python", 32 | "nbconvert_exporter": "python", 33 | "pygments_lexer": "ipython3", 34 | "version": "3.7.3" 35 | } 36 | }, 37 | "nbformat": 4, 38 | "nbformat_minor": 2 39 | } 40 | -------------------------------------------------------------------------------- /Chapter07/unit_tests/.ipynb_checkpoints/Exercise 95-checkpoint.ipynb: -------------------------------------------------------------------------------- 1 | { 2 | "cells": [ 3 | { 4 | "cell_type": "code", 5 | "execution_count": null, 6 | "metadata": {}, 7 | "outputs": [], 8 | "source": [ 9 | "def test_exercise_93_2(x) -> bool:\n", 10 | " import re\n", 11 | " string2 = 'python' \n", 12 | " pattern = r\"Python\"\n", 13 | " return re.match(pattern,string2) == x" 14 | ] 15 | }, 16 | { 17 | "cell_type": "code", 18 | "execution_count": null, 19 | "metadata": {}, 20 | "outputs": [], 21 | "source": [] 22 | } 23 | ], 24 | "metadata": { 25 | "kernelspec": { 26 | "display_name": "Python 3", 27 | "language": "python", 28 | "name": "python3" 29 | }, 30 | "language_info": { 31 | "codemirror_mode": { 32 | "name": "ipython", 33 | "version": 3 34 | }, 35 | "file_extension": ".py", 36 | "mimetype": "text/x-python", 37 | "name": "python", 38 | "nbconvert_exporter": "python", 39 | "pygments_lexer": "ipython3", 40 | "version": "3.7.3" 41 | } 42 | }, 43 | "nbformat": 4, 44 | "nbformat_minor": 2 45 | } 46 | -------------------------------------------------------------------------------- /Chapter07/unit_tests/.ipynb_checkpoints/Exercise 96-checkpoint.ipynb: -------------------------------------------------------------------------------- 1 | { 2 | "cells": [ 3 | { 4 | "cell_type": "code", 5 | "execution_count": null, 6 | "metadata": {}, 7 | "outputs": [], 8 | "source": [ 9 | "def test_exercise_96_2(x) -> bool:\n", 10 | " import re\n", 11 | " string1 = 'Python' \n", 12 | " string2 = 'python'\n", 13 | " pattern = r\"Python\"\n", 14 | " prog = re.compile(pattern)\n", 15 | " return prog.match(string1) == x" 16 | ] 17 | }, 18 | { 19 | "cell_type": "code", 20 | "execution_count": null, 21 | "metadata": {}, 22 | "outputs": [], 23 | "source": [ 24 | "def test_exercise_96_3(x) -> bool:\n", 25 | " import re\n", 26 | " string1 = 'Python' \n", 27 | " string2 = 'python'\n", 28 | " pattern = r\"Python\"\n", 29 | " prog = re.compile(pattern)\n", 30 | " return prog.match(string1) == x" 31 | ] 32 | } 33 | ], 34 | "metadata": { 35 | "kernelspec": { 36 | "display_name": "Python 3", 37 | "language": "python", 38 | "name": "python3" 39 | }, 40 | "language_info": { 41 | "codemirror_mode": { 42 | "name": "ipython", 43 | "version": 3 44 | }, 45 | "file_extension": ".py", 46 | "mimetype": "text/x-python", 47 | "name": "python", 48 | "nbconvert_exporter": "python", 49 | "pygments_lexer": "ipython3", 50 | "version": "3.7.3" 51 | } 52 | }, 53 | "nbformat": 4, 54 | "nbformat_minor": 2 55 | } 56 | -------------------------------------------------------------------------------- /Chapter07/unit_tests/.ipynb_checkpoints/Exercise 97-checkpoint.ipynb: -------------------------------------------------------------------------------- 1 | { 2 | "cells": [ 3 | { 4 | "cell_type": "code", 5 | "execution_count": null, 6 | "metadata": {}, 7 | "outputs": [], 8 | "source": [ 9 | "def test_exercise_97_1(x) -> bool:\n", 10 | " prog = re.compile(r'y') \n", 11 | " return prog.match('Python',pos=1) == x" 12 | ] 13 | }, 14 | { 15 | "cell_type": "code", 16 | "execution_count": null, 17 | "metadata": {}, 18 | "outputs": [], 19 | "source": [ 20 | "def test_exercise_97_2(x) -> bool:\n", 21 | " prog = re.compile(r'thon') \n", 22 | " return prog.match('Python',pos=2) == x" 23 | ] 24 | }, 25 | { 26 | "cell_type": "code", 27 | "execution_count": null, 28 | "metadata": {}, 29 | "outputs": [], 30 | "source": [ 31 | "def test_exercise_97_3(x) -> bool:\n", 32 | " prog = re.compile(r'thon') \n", 33 | " return prog.match('Marathon',pos=4) == x" 34 | ] 35 | } 36 | ], 37 | "metadata": { 38 | "kernelspec": { 39 | "display_name": "Python 3", 40 | "language": "python", 41 | "name": "python3" 42 | }, 43 | "language_info": { 44 | "codemirror_mode": { 45 | "name": "ipython", 46 | "version": 3 47 | }, 48 | "file_extension": ".py", 49 | "mimetype": "text/x-python", 50 | "name": "python", 51 | "nbconvert_exporter": "python", 52 | "pygments_lexer": "ipython3", 53 | "version": "3.7.3" 54 | } 55 | }, 56 | "nbformat": 4, 57 | "nbformat_minor": 2 58 | } 59 | -------------------------------------------------------------------------------- /Chapter07/unit_tests/.ipynb_checkpoints/Exercise 98-checkpoint.ipynb: -------------------------------------------------------------------------------- 1 | { 2 | "cells": [ 3 | { 4 | "cell_type": "code", 5 | "execution_count": null, 6 | "metadata": {}, 7 | "outputs": [], 8 | "source": [ 9 | "def test_exercise_98_1(x) -> bool:\n", 10 | " prog = re.compile('ing') \n", 11 | " return prog.match('Spring') == x" 12 | ] 13 | } 14 | ], 15 | "metadata": { 16 | "kernelspec": { 17 | "display_name": "Python 3", 18 | "language": "python", 19 | "name": "python3" 20 | }, 21 | "language_info": { 22 | "codemirror_mode": { 23 | "name": "ipython", 24 | "version": 3 25 | }, 26 | "file_extension": ".py", 27 | "mimetype": "text/x-python", 28 | "name": "python", 29 | "nbconvert_exporter": "python", 30 | "pygments_lexer": "ipython3", 31 | "version": "3.7.3" 32 | } 33 | }, 34 | "nbformat": 4, 35 | "nbformat_minor": 2 36 | } 37 | -------------------------------------------------------------------------------- /Chapter07/unit_tests/Activity 7.02.ipynb: -------------------------------------------------------------------------------- 1 | { 2 | "cells": [ 3 | { 4 | "cell_type": "code", 5 | "execution_count": null, 6 | "metadata": {}, 7 | "outputs": [], 8 | "source": [ 9 | "def test_exercise_1(x) -> bool:\n", 10 | " import sys\n", 11 | " if \"json\" in sys.modules:\n", 12 | " return False\n", 13 | " else:\n", 14 | " return True" 15 | ] 16 | }, 17 | { 18 | "cell_type": "code", 19 | "execution_count": null, 20 | "metadata": {}, 21 | "outputs": [], 22 | "source": [ 23 | "def test_exercise_2(x) -> bool:\n", 24 | " import urllib.request, urllib.parse, urllib.error \n", 25 | " import json \n", 26 | " with open('APIkeys.json') as f: \n", 27 | " keys = json.load(f) \n", 28 | " omdbapi = keys['OMDBapi'] \n", 29 | " serviceurl = 'http://www.omdbapi.com/?'\n", 30 | " apikey = '&apikey='+omdbapi \n", 31 | " return ctx == x" 32 | ] 33 | } 34 | ], 35 | "metadata": { 36 | "kernelspec": { 37 | "display_name": "Python 3", 38 | "language": "python", 39 | "name": "python3" 40 | }, 41 | "language_info": { 42 | "codemirror_mode": { 43 | "name": "ipython", 44 | "version": 3 45 | }, 46 | "file_extension": ".py", 47 | "mimetype": "text/x-python", 48 | "name": "python", 49 | "nbconvert_exporter": "python", 50 | "pygments_lexer": "ipython3", 51 | "version": "3.7.3" 52 | } 53 | }, 54 | "nbformat": 4, 55 | "nbformat_minor": 2 56 | } 57 | -------------------------------------------------------------------------------- /Chapter07/unit_tests/Exercise 7.07.ipynb: -------------------------------------------------------------------------------- 1 | { 2 | "cells": [ 3 | { 4 | "cell_type": "code", 5 | "execution_count": null, 6 | "metadata": {}, 7 | "outputs": [], 8 | "source": [ 9 | "data = '''\n", 10 | "\n", 11 | " Dave\n", 12 | " Piccardo\n", 13 | " \n", 14 | " +1 742 101 4456\n", 15 | " \n", 16 | " \n", 17 | " dave.p@gmail.com\n", 18 | "'''\n" 19 | ] 20 | }, 21 | { 22 | "cell_type": "code", 23 | "execution_count": null, 24 | "metadata": {}, 25 | "outputs": [], 26 | "source": [ 27 | "import xml.etree.ElementTree as ET\n", 28 | "tree = ET.fromstring(data)\n", 29 | "type (tree)\n" 30 | ] 31 | }, 32 | { 33 | "cell_type": "code", 34 | "execution_count": null, 35 | "metadata": {}, 36 | "outputs": [], 37 | "source": [] 38 | } 39 | ], 40 | "metadata": { 41 | "kernelspec": { 42 | "display_name": "Python 3", 43 | "language": "python", 44 | "name": "python3" 45 | }, 46 | "language_info": { 47 | "codemirror_mode": { 48 | "name": "ipython", 49 | "version": 3 50 | }, 51 | "file_extension": ".py", 52 | "mimetype": "text/x-python", 53 | "name": "python", 54 | "nbconvert_exporter": "python", 55 | "pygments_lexer": "ipython3", 56 | "version": "3.7.3" 57 | } 58 | }, 59 | "nbformat": 4, 60 | "nbformat_minor": 2 61 | } 62 | -------------------------------------------------------------------------------- /Chapter07/unit_tests/Exercise 7.10.ipynb: -------------------------------------------------------------------------------- 1 | { 2 | "cells": [ 3 | { 4 | "cell_type": "code", 5 | "execution_count": null, 6 | "metadata": {}, 7 | "outputs": [], 8 | "source": [ 9 | "def test_exercise_93_1(x) -> bool:\n", 10 | " import requests, urllib.parse\n", 11 | " serviceurl = 'https://restcountries.eu/rest/v2/name/'\n", 12 | " url = serviceurl + country_name \n", 13 | " uh = urllib.request.urlopen(url) \n", 14 | " return uh == x" 15 | ] 16 | } 17 | ], 18 | "metadata": { 19 | "kernelspec": { 20 | "display_name": "Python 3", 21 | "language": "python", 22 | "name": "python3" 23 | }, 24 | "language_info": { 25 | "codemirror_mode": { 26 | "name": "ipython", 27 | "version": 3 28 | }, 29 | "file_extension": ".py", 30 | "mimetype": "text/x-python", 31 | "name": "python", 32 | "nbconvert_exporter": "python", 33 | "pygments_lexer": "ipython3", 34 | "version": "3.7.3" 35 | } 36 | }, 37 | "nbformat": 4, 38 | "nbformat_minor": 2 39 | } 40 | -------------------------------------------------------------------------------- /Chapter07/unit_tests/Exercise 7.11.ipynb: -------------------------------------------------------------------------------- 1 | { 2 | "cells": [ 3 | { 4 | "cell_type": "code", 5 | "execution_count": 1, 6 | "metadata": {}, 7 | "outputs": [], 8 | "source": [ 9 | "def test_exercise_95_2(x) -> bool:\n", 10 | " import re\n", 11 | " string2 = 'python' \n", 12 | " pattern = r\"Python\"\n", 13 | " return re.match(pattern,string2) == x" 14 | ] 15 | }, 16 | { 17 | "cell_type": "code", 18 | "execution_count": null, 19 | "metadata": {}, 20 | "outputs": [], 21 | "source": [] 22 | } 23 | ], 24 | "metadata": { 25 | "kernelspec": { 26 | "display_name": "Python 3", 27 | "language": "python", 28 | "name": "python3" 29 | }, 30 | "language_info": { 31 | "codemirror_mode": { 32 | "name": "ipython", 33 | "version": 3 34 | }, 35 | "file_extension": ".py", 36 | "mimetype": "text/x-python", 37 | "name": "python", 38 | "nbconvert_exporter": "python", 39 | "pygments_lexer": "ipython3", 40 | "version": "3.7.3" 41 | } 42 | }, 43 | "nbformat": 4, 44 | "nbformat_minor": 2 45 | } 46 | -------------------------------------------------------------------------------- /Chapter07/unit_tests/Exercise 7.12.ipynb: -------------------------------------------------------------------------------- 1 | { 2 | "cells": [ 3 | { 4 | "cell_type": "code", 5 | "execution_count": null, 6 | "metadata": {}, 7 | "outputs": [], 8 | "source": [ 9 | "def test_exercise_96_2(x) -> bool:\n", 10 | " import re\n", 11 | " string1 = 'Python' \n", 12 | " string2 = 'python'\n", 13 | " pattern = r\"Python\"\n", 14 | " prog = re.compile(pattern)\n", 15 | " return prog.match(string1) == x" 16 | ] 17 | }, 18 | { 19 | "cell_type": "code", 20 | "execution_count": null, 21 | "metadata": {}, 22 | "outputs": [], 23 | "source": [ 24 | "def test_exercise_96_3(x) -> bool:\n", 25 | " import re\n", 26 | " string1 = 'Python' \n", 27 | " string2 = 'python'\n", 28 | " pattern = r\"Python\"\n", 29 | " prog = re.compile(pattern)\n", 30 | " return prog.match(string2) == x" 31 | ] 32 | } 33 | ], 34 | "metadata": { 35 | "kernelspec": { 36 | "display_name": "Python 3", 37 | "language": "python", 38 | "name": "python3" 39 | }, 40 | "language_info": { 41 | "codemirror_mode": { 42 | "name": "ipython", 43 | "version": 3 44 | }, 45 | "file_extension": ".py", 46 | "mimetype": "text/x-python", 47 | "name": "python", 48 | "nbconvert_exporter": "python", 49 | "pygments_lexer": "ipython3", 50 | "version": "3.7.3" 51 | } 52 | }, 53 | "nbformat": 4, 54 | "nbformat_minor": 2 55 | } 56 | -------------------------------------------------------------------------------- /Chapter07/unit_tests/Exercise 7.13.ipynb: -------------------------------------------------------------------------------- 1 | { 2 | "cells": [ 3 | { 4 | "cell_type": "code", 5 | "execution_count": null, 6 | "metadata": {}, 7 | "outputs": [], 8 | "source": [ 9 | "def test_exercise_97_1(x) -> bool:\n", 10 | " prog = re.compile(r'y') \n", 11 | " return prog.match('Python',pos=1) == x" 12 | ] 13 | }, 14 | { 15 | "cell_type": "code", 16 | "execution_count": null, 17 | "metadata": {}, 18 | "outputs": [], 19 | "source": [ 20 | "def test_exercise_97_2(x) -> bool:\n", 21 | " prog = re.compile(r'thon') \n", 22 | " return prog.match('Python',pos=2) == x" 23 | ] 24 | }, 25 | { 26 | "cell_type": "code", 27 | "execution_count": null, 28 | "metadata": {}, 29 | "outputs": [], 30 | "source": [ 31 | "def test_exercise_97_3(x) -> bool:\n", 32 | " prog = re.compile(r'thon') \n", 33 | " return prog.match('Marathon',pos=4) == x" 34 | ] 35 | } 36 | ], 37 | "metadata": { 38 | "kernelspec": { 39 | "display_name": "Python 3", 40 | "language": "python", 41 | "name": "python3" 42 | }, 43 | "language_info": { 44 | "codemirror_mode": { 45 | "name": "ipython", 46 | "version": 3 47 | }, 48 | "file_extension": ".py", 49 | "mimetype": "text/x-python", 50 | "name": "python", 51 | "nbconvert_exporter": "python", 52 | "pygments_lexer": "ipython3", 53 | "version": "3.7.3" 54 | } 55 | }, 56 | "nbformat": 4, 57 | "nbformat_minor": 2 58 | } 59 | -------------------------------------------------------------------------------- /Chapter07/unit_tests/Exercise 7.14.ipynb: -------------------------------------------------------------------------------- 1 | { 2 | "cells": [ 3 | { 4 | "cell_type": "code", 5 | "execution_count": null, 6 | "metadata": {}, 7 | "outputs": [], 8 | "source": [ 9 | "def test_exercise_98_1(x) -> bool:\n", 10 | " prog = re.compile('ing') \n", 11 | " return prog.match('Spring') == x" 12 | ] 13 | } 14 | ], 15 | "metadata": { 16 | "kernelspec": { 17 | "display_name": "Python 3", 18 | "language": "python", 19 | "name": "python3" 20 | }, 21 | "language_info": { 22 | "codemirror_mode": { 23 | "name": "ipython", 24 | "version": 3 25 | }, 26 | "file_extension": ".py", 27 | "mimetype": "text/x-python", 28 | "name": "python", 29 | "nbconvert_exporter": "python", 30 | "pygments_lexer": "ipython3", 31 | "version": "3.7.3" 32 | } 33 | }, 34 | "nbformat": 4, 35 | "nbformat_minor": 2 36 | } 37 | -------------------------------------------------------------------------------- /Chapter08/.ipynb_checkpoints/Exercise 8.01-checkpoint.ipynb: -------------------------------------------------------------------------------- 1 | { 2 | "cells": [ 3 | { 4 | "cell_type": "code", 5 | "execution_count": 1, 6 | "metadata": {}, 7 | "outputs": [], 8 | "source": [ 9 | "import sqlite3 " 10 | ] 11 | }, 12 | { 13 | "cell_type": "code", 14 | "execution_count": 2, 15 | "metadata": {}, 16 | "outputs": [], 17 | "source": [ 18 | "conn = sqlite3.connect(\"chapter.db\") " 19 | ] 20 | }, 21 | { 22 | "cell_type": "code", 23 | "execution_count": 3, 24 | "metadata": {}, 25 | "outputs": [], 26 | "source": [ 27 | "conn.close() " 28 | ] 29 | }, 30 | { 31 | "cell_type": "code", 32 | "execution_count": 4, 33 | "metadata": {}, 34 | "outputs": [], 35 | "source": [ 36 | "#Alternative method\n", 37 | "with sqlite3.connect(\"chapter.db\") as conn: \n", 38 | " pass " 39 | ] 40 | }, 41 | { 42 | "cell_type": "code", 43 | "execution_count": null, 44 | "metadata": {}, 45 | "outputs": [], 46 | "source": [] 47 | } 48 | ], 49 | "metadata": { 50 | "kernelspec": { 51 | "display_name": "Python 3", 52 | "language": "python", 53 | "name": "python3" 54 | }, 55 | "language_info": { 56 | "codemirror_mode": { 57 | "name": "ipython", 58 | "version": 3 59 | }, 60 | "file_extension": ".py", 61 | "mimetype": "text/x-python", 62 | "name": "python", 63 | "nbconvert_exporter": "python", 64 | "pygments_lexer": "ipython3", 65 | "version": "3.7.3" 66 | } 67 | }, 68 | "nbformat": 4, 69 | "nbformat_minor": 2 70 | } 71 | -------------------------------------------------------------------------------- /Chapter08/Activity 8.01/petsdb: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/PacktWorkshops/The-Data-Wrangling-Workshop/ddad20f8676602ac6624e72e802769fcaff45b0f/Chapter08/Activity 8.01/petsdb -------------------------------------------------------------------------------- /Chapter08/Exercise 8.01/Exercise 8.01.ipynb: -------------------------------------------------------------------------------- 1 | { 2 | "cells": [ 3 | { 4 | "cell_type": "code", 5 | "execution_count": 7, 6 | "metadata": {}, 7 | "outputs": [], 8 | "source": [ 9 | "import sqlite3 " 10 | ] 11 | }, 12 | { 13 | "cell_type": "code", 14 | "execution_count": 8, 15 | "metadata": {}, 16 | "outputs": [], 17 | "source": [ 18 | "conn = sqlite3.connect(\"../lesson.db\") " 19 | ] 20 | }, 21 | { 22 | "cell_type": "code", 23 | "execution_count": 9, 24 | "metadata": {}, 25 | "outputs": [], 26 | "source": [ 27 | "conn.close() " 28 | ] 29 | }, 30 | { 31 | "cell_type": "code", 32 | "execution_count": 11, 33 | "metadata": {}, 34 | "outputs": [], 35 | "source": [ 36 | "#Better approach\n", 37 | "with sqlite3.connect(\"../lesson.db\") as conn: \n", 38 | " pass " 39 | ] 40 | } 41 | ], 42 | "metadata": { 43 | "kernelspec": { 44 | "display_name": "Python 3", 45 | "language": "python", 46 | "name": "python3" 47 | }, 48 | "language_info": { 49 | "codemirror_mode": { 50 | "name": "ipython", 51 | "version": 3 52 | }, 53 | "file_extension": ".py", 54 | "mimetype": "text/x-python", 55 | "name": "python", 56 | "nbconvert_exporter": "python", 57 | "pygments_lexer": "ipython3", 58 | "version": "3.7.4" 59 | } 60 | }, 61 | "nbformat": 4, 62 | "nbformat_minor": 2 63 | } 64 | -------------------------------------------------------------------------------- /Chapter08/petsdb: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/PacktWorkshops/The-Data-Wrangling-Workshop/ddad20f8676602ac6624e72e802769fcaff45b0f/Chapter08/petsdb -------------------------------------------------------------------------------- /Chapter08/petsdb.db: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/PacktWorkshops/The-Data-Wrangling-Workshop/ddad20f8676602ac6624e72e802769fcaff45b0f/Chapter08/petsdb.db -------------------------------------------------------------------------------- /Chapter08/unit_tests/.ipynb_checkpoints/Exercise 8.01-checkpoint.ipynb: -------------------------------------------------------------------------------- 1 | { 2 | "cells": [ 3 | { 4 | "cell_type": "code", 5 | "execution_count": 1, 6 | "metadata": {}, 7 | "outputs": [], 8 | "source": [ 9 | "def test_exercise_1(x) -> bool:\n", 10 | " import sys\n", 11 | " if \"sqlite3\" in sys.modules:\n", 12 | " return False\n", 13 | " else:\n", 14 | " return True" 15 | ] 16 | }, 17 | { 18 | "cell_type": "code", 19 | "execution_count": null, 20 | "metadata": {}, 21 | "outputs": [], 22 | "source": [ 23 | "def test_exercise_2(x) -> bool:\n", 24 | " conn = sqlite3.connect(\"chapter.db\") \n", 25 | " return conn == x" 26 | ] 27 | } 28 | ], 29 | "metadata": { 30 | "kernelspec": { 31 | "display_name": "Python 3", 32 | "language": "python", 33 | "name": "python3" 34 | }, 35 | "language_info": { 36 | "codemirror_mode": { 37 | "name": "ipython", 38 | "version": 3 39 | }, 40 | "file_extension": ".py", 41 | "mimetype": "text/x-python", 42 | "name": "python", 43 | "nbconvert_exporter": "python", 44 | "pygments_lexer": "ipython3", 45 | "version": "3.7.3" 46 | } 47 | }, 48 | "nbformat": 4, 49 | "nbformat_minor": 2 50 | } 51 | -------------------------------------------------------------------------------- /Chapter08/unit_tests/.ipynb_checkpoints/exercise 8.02-checkpoint.ipynb: -------------------------------------------------------------------------------- 1 | { 2 | "cells": [ 3 | { 4 | "cell_type": "code", 5 | "execution_count": null, 6 | "metadata": {}, 7 | "outputs": [], 8 | "source": [ 9 | "def test_exercise_2(path_to_db) -> bool:\n", 10 | " import sqlite3\n", 11 | " import pandas as pd\n", 12 | " conn = sqlite3.connect(path_to_db)\n", 13 | " df = pd.read_sql_query('''SELECT * FROM user;''', conn)\n", 14 | " return \"bob@example.com\" in df[\"email\"].unique()\n", 15 | " \n", 16 | " " 17 | ] 18 | } 19 | ], 20 | "metadata": { 21 | "kernelspec": { 22 | "display_name": "Python 3", 23 | "language": "python", 24 | "name": "python3" 25 | }, 26 | "language_info": { 27 | "codemirror_mode": { 28 | "name": "ipython", 29 | "version": 3 30 | }, 31 | "file_extension": ".py", 32 | "mimetype": "text/x-python", 33 | "name": "python", 34 | "nbconvert_exporter": "python", 35 | "pygments_lexer": "ipython3", 36 | "version": "3.7.3" 37 | } 38 | }, 39 | "nbformat": 4, 40 | "nbformat_minor": 2 41 | } 42 | -------------------------------------------------------------------------------- /Chapter08/unit_tests/Exercise 8.01.ipynb: -------------------------------------------------------------------------------- 1 | { 2 | "cells": [ 3 | { 4 | "cell_type": "code", 5 | "execution_count": 1, 6 | "metadata": {}, 7 | "outputs": [], 8 | "source": [ 9 | "def test_exercise_1(x) -> bool:\n", 10 | " import sys\n", 11 | " if \"sqlite3\" in sys.modules:\n", 12 | " return False\n", 13 | " else:\n", 14 | " return True" 15 | ] 16 | }, 17 | { 18 | "cell_type": "code", 19 | "execution_count": null, 20 | "metadata": {}, 21 | "outputs": [], 22 | "source": [ 23 | "def test_exercise_2(x) -> bool:\n", 24 | " conn = sqlite3.connect(\"chapter.db\") \n", 25 | " return conn == x" 26 | ] 27 | } 28 | ], 29 | "metadata": { 30 | "kernelspec": { 31 | "display_name": "Python 3", 32 | "language": "python", 33 | "name": "python3" 34 | }, 35 | "language_info": { 36 | "codemirror_mode": { 37 | "name": "ipython", 38 | "version": 3 39 | }, 40 | "file_extension": ".py", 41 | "mimetype": "text/x-python", 42 | "name": "python", 43 | "nbconvert_exporter": "python", 44 | "pygments_lexer": "ipython3", 45 | "version": "3.7.3" 46 | } 47 | }, 48 | "nbformat": 4, 49 | "nbformat_minor": 2 50 | } 51 | -------------------------------------------------------------------------------- /Chapter08/unit_tests/Exercise 8.04.ipynb: -------------------------------------------------------------------------------- 1 | { 2 | "cells": [ 3 | { 4 | "cell_type": "code", 5 | "execution_count": null, 6 | "metadata": {}, 7 | "outputs": [], 8 | "source": [ 9 | "def test_exercise_4_3(path_to_db) -> bool:\n", 10 | " import sqlite3\n", 11 | " import pandas as pd\n", 12 | " conn = sqlite3.connect(path_to_db)\n", 13 | " df = pd.read_sql_query('''SELECT * FROM user;''', conn)\n", 14 | " return \"m\" in df[\"gender\"].unique()\n", 15 | " \n", 16 | " " 17 | ] 18 | }, 19 | { 20 | "cell_type": "code", 21 | "execution_count": null, 22 | "metadata": {}, 23 | "outputs": [], 24 | "source": [ 25 | "def test_exercise_4_4(path_to_db,rows ) -> bool:\n", 26 | " import sqlite3\n", 27 | " import pandas as pd\n", 28 | " with sqlite3.connect(path_to_db) as conn: \n", 29 | " cursor = conn.cursor()\n", 30 | " rows_test = cursor.execute('SELECT * FROM user;')\n", 31 | " return rows_test == rows" 32 | ] 33 | } 34 | ], 35 | "metadata": { 36 | "kernelspec": { 37 | "display_name": "Python 3", 38 | "language": "python", 39 | "name": "python3" 40 | }, 41 | "language_info": { 42 | "codemirror_mode": { 43 | "name": "ipython", 44 | "version": 3 45 | }, 46 | "file_extension": ".py", 47 | "mimetype": "text/x-python", 48 | "name": "python", 49 | "nbconvert_exporter": "python", 50 | "pygments_lexer": "ipython3", 51 | "version": "3.7.3" 52 | } 53 | }, 54 | "nbformat": 4, 55 | "nbformat_minor": 2 56 | } 57 | -------------------------------------------------------------------------------- /Chapter08/unit_tests/exercise 8.02.ipynb: -------------------------------------------------------------------------------- 1 | { 2 | "cells": [ 3 | { 4 | "cell_type": "code", 5 | "execution_count": null, 6 | "metadata": {}, 7 | "outputs": [], 8 | "source": [ 9 | "def test_exercise_2(path_to_db) -> bool:\n", 10 | " import sqlite3\n", 11 | " import pandas as pd\n", 12 | " conn = sqlite3.connect(path_to_db)\n", 13 | " df = pd.read_sql_query('''SELECT * FROM user;''', conn)\n", 14 | " return \"bob@example.com\" in df[\"email\"].unique()\n", 15 | " \n", 16 | " " 17 | ] 18 | } 19 | ], 20 | "metadata": { 21 | "kernelspec": { 22 | "display_name": "Python 3", 23 | "language": "python", 24 | "name": "python3" 25 | }, 26 | "language_info": { 27 | "codemirror_mode": { 28 | "name": "ipython", 29 | "version": 3 30 | }, 31 | "file_extension": ".py", 32 | "mimetype": "text/x-python", 33 | "name": "python", 34 | "nbconvert_exporter": "python", 35 | "pygments_lexer": "ipython3", 36 | "version": "3.7.3" 37 | } 38 | }, 39 | "nbformat": 4, 40 | "nbformat_minor": 2 41 | } 42 | -------------------------------------------------------------------------------- /Chapter09/.ipynb_checkpoints/Activity 9.04-checkpoint.ipynb: -------------------------------------------------------------------------------- 1 | { 2 | "cells": [], 3 | "metadata": {}, 4 | "nbformat": 4, 5 | "nbformat_minor": 2 6 | } 7 | -------------------------------------------------------------------------------- /Chapter09/datasets/SYB61_T07_Education.csv: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/PacktWorkshops/The-Data-Wrangling-Workshop/ddad20f8676602ac6624e72e802769fcaff45b0f/Chapter09/datasets/SYB61_T07_Education.csv -------------------------------------------------------------------------------- /Chapter09/unit tests/Activity 9.04.ipynb: -------------------------------------------------------------------------------- 1 | { 2 | "cells": [ 3 | { 4 | "cell_type": "code", 5 | "execution_count": null, 6 | "metadata": {}, 7 | "outputs": [], 8 | "source": [ 9 | "def test_exercise_1(path_to_db) -> bool:\n", 10 | " import sqlite3\n", 11 | " import pandas as pd\n", 12 | " conn = sqlite3.connect(path_to_db)\n", 13 | " df = pd.read_sql_query('''SELECT * FROM education_gdp;''', conn)\n", 14 | " return not \"2003\" in df[\"Year\"].unique()" 15 | ] 16 | } 17 | ], 18 | "metadata": { 19 | "kernelspec": { 20 | "display_name": "Python 3", 21 | "language": "python", 22 | "name": "python3" 23 | }, 24 | "language_info": { 25 | "codemirror_mode": { 26 | "name": "ipython", 27 | "version": 3 28 | }, 29 | "file_extension": ".py", 30 | "mimetype": "text/x-python", 31 | "name": "python", 32 | "nbconvert_exporter": "python", 33 | "pygments_lexer": "ipython3", 34 | "version": "3.7.3" 35 | } 36 | }, 37 | "nbformat": 4, 38 | "nbformat_minor": 2 39 | } 40 | -------------------------------------------------------------------------------- /LICENSE: -------------------------------------------------------------------------------- 1 | MIT License 2 | 3 | Copyright (c) 2020 Packt Workshops 4 | 5 | Permission is hereby granted, free of charge, to any person obtaining a copy 6 | of this software and associated documentation files (the "Software"), to deal 7 | in the Software without restriction, including without limitation the rights 8 | to use, copy, modify, merge, publish, distribute, sublicense, and/or sell 9 | copies of the Software, and to permit persons to whom the Software is 10 | furnished to do so, subject to the following conditions: 11 | 12 | The above copyright notice and this permission notice shall be included in all 13 | copies or substantial portions of the Software. 14 | 15 | THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR 16 | IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, 17 | FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE 18 | AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER 19 | LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, 20 | OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE 21 | SOFTWARE. 22 | -------------------------------------------------------------------------------- /requirements.txt: -------------------------------------------------------------------------------- 1 | backcall==0.1.0 2 | beautifulsoup4==4.6.3 3 | BeautifulSoap 4 | bleach==2.1.4 5 | certifi==2018.8.24 6 | chardet==3.0.4 7 | cycler==0.10.0 8 | decorator==4.3.0 9 | entrypoints==0.2.3 10 | html5lib==1.0.1 11 | idna==2.7 12 | ipykernel==4.9.0 13 | ipython==6.5.0 14 | ipython-genutils==0.2.0 15 | ipywidgets==7.4.1 16 | jedi==0.12.1 17 | Jinja2==2.10 18 | jsonschema==2.6.0 19 | jupyter==1.0.0 20 | jupyter-client==5.2.3 21 | jupyter-console==5.2.0 22 | jupyter-core==4.4.0 23 | kiwisolver==1.0.1 24 | MarkupSafe==1.0 25 | matplotlib==2.2.3 26 | mistune==0.8.3 27 | nbconvert==5.3.1 28 | nbformat==4.4.0 29 | notebook==5.6.0 30 | numpy==1.15.1 31 | pandas==0.23.4 32 | pandocfilters==1.4.2 33 | parso==0.3.1 34 | pexpect==4.6.0 35 | pickleshare==0.7.4 36 | prometheus-client==0.3.1 37 | prompt-toolkit==1.0.15 38 | ptyprocess==0.6.0 39 | Pygments==2.2.0 40 | pyparsing==2.2.0 41 | python-dateutil==2.7.3 42 | pytz==2018.5 43 | pyzmq==17.1.2 44 | qtconsole==4.4.1 45 | requests==2.19.1 46 | Send2Trash==1.5.0 47 | simplegeneric==0.8.1 48 | six==1.11.0 49 | sqlite 50 | terminado==0.8.1 51 | testpath==0.3.1 52 | tornado==5.1 53 | traitlets==4.3.2 54 | urllib3==1.23 55 | wcwidth==0.1.7 56 | webencodings==0.5.1 57 | widgetsnbextension==3.4.1 58 | xlrd 59 | --------------------------------------------------------------------------------