├── 10_pandasremovingduplicates.py ├── 1_pandasintro.py ├── 2_pandasseries.py ├── 3_pandasdataframe.py ├── 4_pandasreadcsv.py ├── 5_pandasreadjson.py ├── 6_pandasanalyzingdataframe.py ├── 7_pandascleaningdata.py ├── 8_pandascleaningwrongformat.py ├── 9_pandasfixingwrongdata.py ├── LICENSE ├── Python pandas Introduction.pdf ├── Python pandas tutorial.png ├── data.csv ├── data.js └── dirtydata.csv /10_pandasremovingduplicates.py: -------------------------------------------------------------------------------- 1 | # removing the duplicate values: 1st you need to discover the duplicate values via duplicated() method. 2 | 3 | # loading and reading the original dataframe 4 | import pandas as pd 5 | sharad = pd.read_csv('dirtydata.csv') 6 | print(sharad.to_string()) 7 | 8 | # returns True for every row that is duplicate otherwise return false: 9 | import pandas as pd 10 | sharad = pd.read_csv('dirtydata.csv') 11 | print(sharad.duplicated()) 12 | 13 | # removing the duplicate from the data set. via drop_duplicates() 14 | import pandas as pd 15 | sharad = pd.read_csv('dirtydata.csv') 16 | sharad.drop_duplicates(inplace=True) 17 | print(sharad.to_string()) 18 | -------------------------------------------------------------------------------- /1_pandasintro.py: -------------------------------------------------------------------------------- 1 | import pandas as pd 2 | print(pd.__version__) -------------------------------------------------------------------------------- /2_pandasseries.py: -------------------------------------------------------------------------------- 1 | # A Pandas series is like a column in a table . it is 1D array which holds data of any type. 2 | # here we will create a simple pandas series. 3 | import pandas as pd 4 | sharad = [1,7,2] 5 | sharadnew = pd.Series(sharad) 6 | print(sharadnew) 7 | 8 | # labeling - label can be use to access a specified value. 9 | import pandas as pd 10 | sharad = [1,7,2] 11 | sharadnew = pd.Series(sharad) 12 | print(sharadnew[0]) 13 | 14 | # wuth Create label you can create your own name labels: 15 | import pandas as pd 16 | sharad = [1,7,2] 17 | sharadnew = pd.Series(sharad, index=["x", "y", "z"]) 18 | print(sharadnew) 19 | 20 | # labeling - label can be use to access a specified value.(after creating own label) 21 | import pandas as pd 22 | sharad = [1,7,2] 23 | sharadnew = pd.Series(sharad, index=["x", "y", "z"]) 24 | print(sharadnew["x"]) 25 | 26 | # you can also use a key or value object like a dictionary, when creating a series. 27 | # here we will create a simple pandas series from a dictionary. 28 | import pandas as pd 29 | cal = {"day1": 420, "day2":380, "day3":390} 30 | sharadnew = pd.Series(cal) 31 | print(sharadnew) 32 | 33 | # now we will create a series using only data from day1 and day2 34 | import pandas as pd 35 | cal = {"day1": 420, "day2":380, "day3":390} 36 | result = pd.Series(cal, index=["day1", "day2"]) 37 | print(result) 38 | 39 | # DataFrame: Data sets in pandas are usually multidimentional tables,, and they re called DataFrames. 40 | # series are like columns and dataframes is the whole table. 41 | 42 | # we will now create a dataframe from 2 series. 43 | import pandas as pd 44 | sharad = {"cal": [420, 380, 390], "duration": [50, 40, 45]} 45 | sharadnew = pd.DataFrame(sharad) 46 | print(sharadnew) 47 | 48 | -------------------------------------------------------------------------------- /3_pandasdataframe.py: -------------------------------------------------------------------------------- 1 | # Dataframe: it is a 2D data structure like a 2D array with table incl. rows and columns. 2 | import pandas as pd 3 | data = {"cal": [420, 380, 390], "dur":[50, 40, 45]} 4 | sharad = pd.DataFrame(data) 5 | print(sharad) 6 | 7 | # Locate row: pandas use the loc attibute to return one or more specified row. 8 | 9 | import pandas as pd 10 | data = {"cal": [420, 380, 390], "dur":[50, 40, 45]} 11 | sharad = pd.DataFrame(data) 12 | print(sharad.loc[0]) 13 | 14 | # example of returning row 0 and 1 15 | import pandas as pd 16 | data = {"cal": [420, 380, 390], "dur":[50, 40, 45]} 17 | sharad = pd.DataFrame(data) 18 | print(sharad.loc[[0,1]]) 19 | 20 | # named Index: with the index arg, you can name your own index. 21 | import pandas as pd 22 | data = {"cal": [420, 380, 390], "dur":[50, 40, 45]} 23 | sharad = pd.DataFrame(data, index=["day1", "day2", "day3"]) 24 | print(sharad) 25 | 26 | # locate the named index: 27 | import pandas as pd 28 | data = {"cal": [420, 380, 390], "dur":[50, 40, 45]} 29 | sharad = pd.DataFrame(data, index=["day1", "day2", "day3"]) 30 | print(sharad.loc["day2"]) 31 | 32 | # output in a dataframe: 33 | import pandas as pd 34 | data = {"cal": [420, 380, 390], "dur":[50, 40, 45]} 35 | sharad = pd.DataFrame(data, index=["day1", "day2", "day3"]) 36 | print(sharad.loc[["day1", "day2"]]) 37 | 38 | # load the data from the csv file into dataframe i.e data.csv 39 | import pandas as pd 40 | fileload = pd.read_csv('data.csv') 41 | print(fileload) 42 | -------------------------------------------------------------------------------- /4_pandasreadcsv.py: -------------------------------------------------------------------------------- 1 | # read csv files: (comma seperated file) it is a simple way to store the big and bigest data sets. csv files contains plain text. 2 | # loading the csv into a dataframe with to_string 3 | import pandas as pd 4 | df = pd.read_csv('data.csv') 5 | print(df.to_string()) 6 | 7 | # loading the csv into a dataframe without to_string 8 | import pandas as pd 9 | df = pd.read_csv('data.csv') 10 | print(df) 11 | 12 | # max_rows : you can check your system's maximum rows with: 13 | import pandas as pd 14 | print(pd.options.display.max_rows) 15 | 16 | # yes, we can increase the maximum number of rows to display the entire dataframe. 17 | import pandas as pd 18 | pd.options.display.max_rows = 9999 19 | df = pd.read_csv('data.csv') 20 | print(df) -------------------------------------------------------------------------------- /5_pandasreadjson.py: -------------------------------------------------------------------------------- 1 | # JSON: Big data sets are normally stored and extracted as JSON. JSON is a plain text, but it has a format of an object. 2 | # Loading the JSON into a dataframe: 3 | import pandas as pd 4 | sharad = pd.read_json('data.js') 5 | print(sharad.to_string()) 6 | 7 | # Dictionary as a JSON: if your JSON code is not in a file, but in a python dictionary, then you can do all below: 8 | import pandas as pd 9 | data = { 10 | "Duration":{ 11 | "0":60, 12 | "1":60, 13 | "2":60, 14 | "3":45, 15 | "4":45, 16 | "5":60 17 | }, 18 | "Pulse":{ 19 | "0":110, 20 | "1":117, 21 | "2":103, 22 | "3":109, 23 | "4":117, 24 | "5":102 25 | }, 26 | "Maxpulse":{ 27 | "0":130, 28 | "1":145, 29 | "2":135, 30 | "3":175, 31 | "4":148, 32 | "5":127 33 | }, 34 | "Calories":{ 35 | "0":409.1, 36 | "1":479.0, 37 | "2":340.0, 38 | "3":282.4, 39 | "4":406.0, 40 | "5":300.5 41 | } 42 | } 43 | sharadnew = pd.DataFrame(data) 44 | print(sharadnew) -------------------------------------------------------------------------------- /6_pandasanalyzingdataframe.py: -------------------------------------------------------------------------------- 1 | # Viewing the data : one of the most used method for a quick overview of the dataframe is the head() method. this method returns the headers and a specified number of rows. 2 | # here we will print the 1st 10 rows in the dataframe. 3 | import pandas as pd 4 | sharad = pd.read_csv('data.csv') 5 | print(sharad.head(10)) 6 | 7 | # here we will print the last 10 rows in the dataframe. 8 | import pandas as pd 9 | sharad = pd.read_csv('data.csv') 10 | print(sharad.tail(10)) 11 | 12 | # what if you want the information about the data in the dataframe: via info() 13 | import pandas as pd 14 | df = pd.read_csv('data.csv') 15 | print(df.info()) 16 | 17 | -------------------------------------------------------------------------------- /7_pandascleaningdata.py: -------------------------------------------------------------------------------- 1 | # cleaning data : it means fixing the bad data in your data set. bad data could be: empty cell, data in a wrong format, duplicate data and wrong data. 2 | # emplty cell: it will give you wrong result always, we will have to remove the rows always that contain the bad data. 3 | # loading and reading the original dataframe 4 | import pandas as pd 5 | sharad = pd.read_csv('dirtydata.csv') 6 | print(sharad.to_string()) 7 | 8 | # here we will return a new data frame with no empty cell. 9 | 10 | import pandas as pd 11 | sharad = pd.read_csv('dirtydata.csv') 12 | sharadnew = sharad.dropna() 13 | print(sharadnew.to_string()) 14 | 15 | # if at any case you want to change the original dataframe, than use the inplace=True argument. it will remove the rows containing the NULL(NaN) values. 16 | import pandas as pd 17 | sharad = pd.read_csv('dirtydata.csv') 18 | sharad.dropna(inplace=True) 19 | print(sharad.to_string()) 20 | 21 | # replacing the empty value: we will use the fillna() method which will allow us to replace the empty cell with a value. 22 | import pandas as pd 23 | sharad = pd.read_csv('dirtydata.csv') 24 | sharad.fillna(130, inplace=True) 25 | print(sharad.to_string()) 26 | 27 | # to replace only the empty value for one column , you need to specify the column name. 28 | import pandas as pd 29 | sharad = pd.read_csv('dirtydata.csv') 30 | sharad["Calories"].fillna(130, inplace=True) 31 | print(sharad.to_string()) 32 | 33 | # here we can also replace the empty cell using mean(), median() or mode(). 34 | #calculate the MEAN and replace the empty values with it. 35 | import pandas as pd 36 | sharad = pd.read_csv('dirtydata.csv') 37 | x = sharad["Calories"].mean() 38 | sharad["Calories"].fillna(x, inplace=True) 39 | print(sharad.to_string()) 40 | 41 | # calculate the MEDIAN and replace any empty values in it.: 42 | import pandas as pd 43 | sharad = pd.read_csv('dirtydata.csv') 44 | x = sharad["Calories"].median() 45 | sharad["Calories"].fillna(x, inplace=True) 46 | print(sharad.to_string()) 47 | 48 | # calculate the MODE and replace the empty cell with it. 49 | import pandas as pd 50 | sharad = pd.read_csv('dirtydata.csv') 51 | x = sharad["Calories"].mode()[0] 52 | sharad["Calories"].fillna(x, inplace=True) 53 | print(sharad.to_string()) -------------------------------------------------------------------------------- /8_pandascleaningwrongformat.py: -------------------------------------------------------------------------------- 1 | # Data in a wrong format: to fix this problem, there are 2 ways: removing the rows or convert all the cells in the same format. 2 | # loading and reading the original dataframe 3 | import pandas as pd 4 | sharad = pd.read_csv('dirtydata.csv') 5 | print(sharad.to_string()) 6 | 7 | # now let's try to convert all the cells in the date column into dates.via to_datetime() 8 | import pandas as pd 9 | sharad = pd.read_csv('dirtydata.csv') 10 | sharad["Date"] = pd.to_datetime(sharad['Date']) 11 | print(sharad.to_string()) 12 | 13 | # here now we will remove the rows with a NULL value in the 'Date' column. 14 | import pandas as pd 15 | sharad = pd.read_csv('dirtydata.csv') 16 | sharad['Date'] = pd.to_datetime(sharad['Date']) 17 | sharad.dropna(subset=['Date'], inplace=True) 18 | print(sharad.to_string()) -------------------------------------------------------------------------------- /9_pandasfixingwrongdata.py: -------------------------------------------------------------------------------- 1 | # wrong Data: its only a wrong data 2 | 3 | # loading and reading the original dataframe 4 | import pandas as pd 5 | sharad = pd.read_csv('dirtydata.csv') 6 | print(sharad.to_string()) 7 | 8 | # here we will set "Duration" = 45 in row 7: 9 | import pandas as pd 10 | sharad = pd.read_csv('dirtydata.csv') 11 | sharad.loc[7,'Duration'] = 45 12 | print(sharad.to_string()) 13 | 14 | # for larger data set: now here we will loop through all the values in "Duration" column, if the value is higher than 120 than set it to 120. 15 | import pandas as pd 16 | sharad = pd.read_csv('dirtydata.csv') 17 | for i in sharad.index: 18 | if sharad.loc[i, "Duration"] > 120: 19 | sharad.loc[i, "Duration"] = 120 20 | print(sharad.to_string()) 21 | 22 | # you can also remove the rows for wrong data in larger dataset. 23 | import pandas as pd 24 | sharad = pd.read_csv('dirtydata.csv') 25 | for i in sharad.index: 26 | if sharad.loc[i, "Duration"] > 120: 27 | sharad.drop(i, inplace=True) 28 | print(sharad.to_string()) -------------------------------------------------------------------------------- /LICENSE: -------------------------------------------------------------------------------- 1 | GNU GENERAL PUBLIC LICENSE 2 | Version 3, 29 June 2007 3 | 4 | Copyright (C) 2007 Free Software Foundation, Inc. 5 | Everyone is permitted to copy and distribute verbatim copies 6 | of this license document, but changing it is not allowed. 7 | 8 | Preamble 9 | 10 | The GNU General Public License is a free, copyleft license for 11 | software and other kinds of works. 12 | 13 | The licenses for most software and other practical works are designed 14 | to take away your freedom to share and change the works. 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Interpretation of Sections 15 and 16. 613 | 614 | If the disclaimer of warranty and limitation of liability provided 615 | above cannot be given local legal effect according to their terms, 616 | reviewing courts shall apply local law that most closely approximates 617 | an absolute waiver of all civil liability in connection with the 618 | Program, unless a warranty or assumption of liability accompanies a 619 | copy of the Program in return for a fee. 620 | 621 | END OF TERMS AND CONDITIONS 622 | 623 | How to Apply These Terms to Your New Programs 624 | 625 | If you develop a new program, and you want it to be of the greatest 626 | possible use to the public, the best way to achieve this is to make it 627 | free software which everyone can redistribute and change under these terms. 628 | 629 | To do so, attach the following notices to the program. It is safest 630 | to attach them to the start of each source file to most effectively 631 | state the exclusion of warranty; and each file should have at least 632 | the "copyright" line and a pointer to where the full notice is found. 633 | 634 | 635 | Copyright (C) 636 | 637 | This program is free software: you can redistribute it and/or modify 638 | it under the terms of the GNU General Public License as published by 639 | the Free Software Foundation, either version 3 of the License, or 640 | (at your option) any later version. 641 | 642 | This program is distributed in the hope that it will be useful, 643 | but WITHOUT ANY WARRANTY; without even the implied warranty of 644 | MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the 645 | GNU General Public License for more details. 646 | 647 | You should have received a copy of the GNU General Public License 648 | along with this program. If not, see . 649 | 650 | Also add information on how to contact you by electronic and paper mail. 651 | 652 | If the program does terminal interaction, make it output a short 653 | notice like this when it starts in an interactive mode: 654 | 655 | Copyright (C) 656 | This program comes with ABSOLUTELY NO WARRANTY; for details type `show w'. 657 | This is free software, and you are welcome to redistribute it 658 | under certain conditions; type `show c' for details. 659 | 660 | The hypothetical commands `show w' and `show c' should show the appropriate 661 | parts of the General Public License. Of course, your program's commands 662 | might be different; for a GUI interface, you would use an "about box". 663 | 664 | You should also get your employer (if you work as a programmer) or school, 665 | if any, to sign a "copyright disclaimer" for the program, if necessary. 666 | For more information on this, and how to apply and follow the GNU GPL, see 667 | . 668 | 669 | The GNU General Public License does not permit incorporating your program 670 | into proprietary programs. If your program is a subroutine library, you 671 | may consider it more useful to permit linking proprietary applications with 672 | the library. If this is what you want to do, use the GNU Lesser General 673 | Public License instead of this License. But first, please read 674 | . 675 | -------------------------------------------------------------------------------- /Python pandas Introduction.pdf: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/sharadkhare/Complete-Python-Pandas-Tutorial-in-Hindi-With-Notes-/bf2abd69ee73568b8b7364c98738feb80d834399/Python pandas Introduction.pdf -------------------------------------------------------------------------------- /Python pandas tutorial.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/sharadkhare/Complete-Python-Pandas-Tutorial-in-Hindi-With-Notes-/bf2abd69ee73568b8b7364c98738feb80d834399/Python pandas tutorial.png -------------------------------------------------------------------------------- /data.csv: -------------------------------------------------------------------------------- 1 | Duration,Pulse,Maxpulse,Calories 2 | 60,110,130,409.1 3 | 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680 | "164":290.8, 681 | "165":300.4, 682 | "166":310.2, 683 | "167":320.4, 684 | "168":330.4 685 | } 686 | } -------------------------------------------------------------------------------- /dirtydata.csv: -------------------------------------------------------------------------------- 1 | Duration,Date,Pulse,Maxpulse,Calories 2 | 60,'2020/12/01',110,130,409.1 3 | 60,'2020/12/02',117,145,479.0 4 | 60,'2020/12/03',103,135,340.0 5 | 45,'2020/12/04',109,175,282.4 6 | 45,'2020/12/05',117,148,406.0 7 | 60,'2020/12/06',102,127,300.0 8 | 60,'2020/12/07',110,136,374.0 9 | 450,'2020/12/08',104,134,253.3 10 | 30,'2020/12/09',109,133,195.1 11 | 60,'2020/12/10',98,124,269.0 12 | 60,'2020/12/11',103,147,329.3 13 | 60,'2020/12/12',100,120,250.7 14 | 60,'2020/12/12',100,120,250.7 15 | 60,'2020/12/13',106,128,345.3 16 | 60,'2020/12/14',104,132,379.3 17 | 60,'2020/12/15',98,123,275.0 18 | 60,'2020/12/16',98,120,215.2 19 | 60,'2020/12/17',100,120,300.0 20 | 45,'2020/12/18',90,112, 21 | 60,'2020/12/19',103,123,323.0 22 | 45,'2020/12/20',97,125,243.0 23 | 60,'2020/12/21',108,131,364.2 24 | 45,,100,119,282.0 25 | 60,'2020/12/23',130,101,300.0 26 | 45,'2020/12/24',105,132,246.0 27 | 60,'2020/12/25',102,126,334.5 28 | 60,20201226,100,120,250.0 29 | 60,'2020/12/27',92,118,241.0 30 | 60,'2020/12/28',103,132, 31 | 60,'2020/12/29',100,132,280.0 32 | 60,'2020/12/30',102,129,380.3 33 | 60,'2020/12/31',92,115,243.0 --------------------------------------------------------------------------------