├── .gitattributes ├── .gitignore ├── .idea └── other.xml ├── 2018-08-25_cleaning_data_with_python.ipynb ├── 2019-01-24_pandas_dataframes.ipynb ├── 2019-02-04_manipulating_dataframes_with_pandas.ipynb ├── 2019-02-05_intro_to_python_for_finance.ipynb ├── 2019-02-12_intro_to_databases_in_python.ipynb ├── 2019-03-23_merging_dataframes_with_pandas.ipynb ├── 2019-07-10_statistical_thinking_1.ipynb ├── 2019-07-19_fraud_detection_python.ipynb ├── 2019-11-21_joining_data_in_sql.ipynb ├── 2019-12-13_intro_to_relational_databases.ipynb ├── 2020-03-05_intro_to_data_visualization_in_python.ipynb ├── 2020-03-15_interactive_data_visualization_with_bokeh.ipynb ├── 2020-05-21_intro_to_network_analysis_in_python.ipynb ├── 2020-06-01_statistical_thinking_2.ipynb ├── 2020-10-14_supervised_learning_sklearn.ipynb ├── 2021-03-15_intro_to_shell.ipynb ├── 2021-03-29_unsupervised_learning_in_python.ipynb ├── 2021-04-19_intro_to_deep_learning_in_python.ipynb ├── 2024-01-19_school_budgeting_with_machine_learning_in_python.ipynb ├── 2024-01-29_working_with_the_openai_api.ipynb ├── DataCamp NB Template.ipynb ├── Images ├── 2019-02-04_manipulating_dataframes_with_pandas │ └── 2019-02-12_manipulating_dataframes_with_pandas_certificate.JPG ├── 2019-02-04_pandas_foundations │ └── 2019-02-04_pandas_foundations_certificate.jpg ├── 2019-02-07_intro_to_python_for_finance │ ├── 2019-02-07_intro_to_python_for_finance_certificate.JPG │ ├── intro_to_python_for_finance_histogram.JPG │ └── intro_to_python_for_finance_s_and_p_sectors_pie.JPG ├── 2019-03-23_merging_dataframes_with_pandas │ ├── 2019-05-02_merging_dataframes_with_pandas_certificate.JPG │ └── indices_indexes.JPG ├── 2019-07-10_statistical_thinking_1 │ ├── 2019-11-21_statistical_thinking_in_python_1_certificate.JPG │ ├── area_under_pdf.JPG │ ├── binomial_pmf.JPG │ ├── box_plot.JPG │ ├── cdf.JPG │ ├── cdf_97.JPG │ ├── covariance.JPG │ ├── deutschmark.JPG │ ├── discrete_uniform_pmf.JPG │ ├── exponential_inter-incident_times.JPG │ ├── exponential_pdf.JPG │ ├── interpret_cdf.JPG │ ├── interpret_pdf.JPG │ ├── light_tails.JPG │ ├── no_hitter_ecdf.JPG │ ├── normal_distribution.JPG │ ├── pearson.JPG │ ├── poisson_pmf.JPG │ └── var_covar_by_looking.JPG ├── 2020-03-15_interactive_data_visualization_with_bokeh │ ├── 2020-05-21_certificate.jpg │ └── button_types.JPG ├── 2020-05-21_intro_to_network_analysis_in_python │ ├── 2020-05-31_DataCamp_intro_to_network_analysis_in_python.jpg │ ├── arc_plots.JPG │ ├── bfs.JPG │ ├── circo_plots.JPG │ ├── cliques.JPG │ ├── con_com_sub.JPG │ ├── matrix_plots.JPG │ ├── max_cliques.JPG │ ├── multi-edge.JPG │ ├── network_structure_1.JPG │ ├── network_structure_2.JPG │ ├── singapore_station_map.jpg │ └── star_graphs.JPG ├── 2020-06-01_statistical_thinking_2 │ ├── 2020-10-14_DataCamp_statistical_thinking_in_python_II.jpg │ ├── cdf_bass.JPG │ └── splash.JPG ├── 2020-10-14_supervised_learning_sklearn │ ├── 2021-03-14_DataCamp_supervised_learning_with_scikit_learn.JPG │ ├── 3_3_roc_auc.JPG │ ├── conf_matrix.JPG │ ├── crossvalidation.JPG │ ├── cv_grid.JPG │ ├── cv_grid_empty.JPG │ ├── iris_petal.JPG │ ├── iris_petal_db.JPG │ ├── knn.JPG │ ├── knn3.JPG │ ├── knn5.JPG │ ├── log_reg_linear_decision_boundary.JPG │ ├── loss.JPG │ ├── neighbors.JPG │ ├── neighbors2.JPG │ ├── roc1.JPG │ ├── roc2.JPG │ └── roc3.JPG ├── 2021-03-28_intro_to_shell │ └── 2021-03-28_intro_to_shell.JPG ├── 2021-03-29_unsupervised_learning_python │ ├── 2021-04-19_DataCamp_unsupervised_learning_in_python_certificate.jpg │ ├── cluster_linkage_riddle.png │ ├── dendrogram_distance1.JPG │ ├── dendrogram_distance2.JPG │ ├── dendrogram_height.JPG │ ├── dendrogram_step_by_step.jpg │ ├── dimension_reduction_pca.jpg │ ├── eurovision_2016.jpg │ ├── eurovision_scoring.JPG │ ├── flight_path_dim.jpg │ ├── hierarchy_groups.JPG │ ├── how_many_clusters.JPG │ ├── intermediate_clusterings.JPG │ ├── intrinsic_dim_fish.JPG │ ├── nmf_1.jpg │ ├── nmf_10.jpg │ ├── nmf_11.jpg │ ├── nmf_12.jpg │ ├── nmf_13.jpg │ ├── nmf_14.jpg │ ├── nmf_15.jpg │ ├── nmf_16.jpg │ ├── nmf_2.jpg │ ├── nmf_3.jpg │ ├── nmf_4.jpg │ ├── nmf_5.jpg │ ├── nmf_6.jpg │ ├── nmf_7.jpg │ ├── nmf_8.jpg │ ├── nmf_9.jpg │ ├── pearson_corr_ex.jpg │ ├── piedmont_var.jpg │ ├── principal_components.JPG │ ├── principal_components_2.JPG │ └── word_freq_array.jpg ├── 2021-04-19_intro_to_deep_learning_in_python │ ├── 2024-01-18_DataCamp_intro_to_deep_learning_in_python_certificate.jpg │ ├── dl1.jpg │ ├── dl10.jpg │ ├── dl11.jpg │ ├── dl12.jpg │ ├── dl13.jpg │ ├── dl14.jpg │ ├── dl15.jpg │ ├── dl16.jpg │ ├── dl17.jpg │ ├── dl18.jpg │ ├── dl19.jpg │ ├── dl2.jpg │ ├── dl20.jpg │ ├── dl21.jpg │ ├── dl22.jpg │ ├── dl23.jpg │ ├── dl24.jpg │ ├── dl25.jpg │ ├── dl26.jpg │ ├── dl27.jpg │ ├── dl28.jpg │ ├── dl29.jpg │ ├── dl3.jpg │ ├── dl30.jpg │ ├── dl31.jpg │ ├── dl33.jpg │ ├── dl35.jpg │ ├── dl36.jpg │ ├── dl37.jpg │ ├── dl38.jpg │ ├── dl39.jpg │ ├── dl4.jpg │ ├── dl40.jpg │ ├── dl41.jpg │ ├── dl42.jpg │ ├── dl43.jpg │ ├── dl44.jpg │ ├── dl45.jpg │ ├── dl46.jpg │ ├── dl47.jpg │ ├── dl48.jpg │ ├── dl49.jpg │ ├── dl5.jpg │ ├── dl50.jpg │ ├── dl51.jpg │ ├── dl52.jpg │ ├── dl53.jpg │ ├── dl54.jpg │ ├── dl55.jpg │ ├── dl56.jpg │ ├── dl57.jpg │ ├── dl58.jpg │ ├── dl59.jpg │ ├── dl6.jpg │ ├── dl60.jpg │ ├── dl61.jpg │ ├── dl62.jpg │ ├── dl7.jpg │ ├── dl8.jpg │ ├── dl9.jpg │ ├── gradient_descent.gif │ └── model_summary.jpg ├── 2024-01-19_school_budgeting_with_machine_learning_in_python │ ├── 2024-01-27_DataCamp_school_budgeting_with_machine_learning_in_python.jpg │ ├── sb01.jpg │ ├── sb02.jpg │ ├── sb03.jpg │ ├── sb04.jpg │ └── sb05.jpg ├── 2024-01-29_working_with_the_openai_api │ ├── 2024-01-31_DataCamp_working_with_the_openai_api_certificate.jpg │ ├── oai01.jpg │ ├── oai02.jpg │ ├── oai03.jpg │ ├── oai04.jpg │ ├── oai05.jpg │ ├── oai06.jpg │ ├── oai07.jpg │ ├── oai08.jpg │ ├── oai09.jpg │ └── oai10.jpg ├── fraud_detection │ ├── 2019-07-27_fraud_detection_certificate.JPG │ ├── accuracy.JPG │ ├── cluster_outlier.JPG │ ├── clustering.JPG │ ├── clustering_methods.JPG │ ├── clusters_1.JPG │ ├── clusters_2.JPG │ ├── clusters_3.JPG │ ├── clusters_4.JPG │ ├── confusion_matrix.JPG │ ├── confusion_matrix_advanced.JPG │ ├── decision_tree.JPG │ ├── elbow.JPG │ ├── ensemble.JPG │ ├── ensemble_stacking.JPG │ ├── k-means.JPG │ ├── lda.JPG │ ├── logistic_regression.JPG │ ├── neural_network.JPG │ ├── oversampling.JPG │ ├── oversampling_plot.JPG │ ├── precision_recall_inverse.JPG │ ├── random_forest.JPG │ ├── roc_curve.JPG │ ├── rules_based.JPG │ ├── smote.JPG │ ├── text_df.JPG │ ├── text_processed.JPG │ ├── topics.JPG │ ├── topics2.jpg │ ├── typical_data.JPG │ └── undersampling.JPG ├── into_to_relational_databases_in_sql │ ├── 2020-03-05_certificate.jpg │ ├── database_keys.JPG │ ├── df_model.JPG │ ├── ecosystem.JPG │ ├── final_model.JPG │ ├── final_model_2.JPG │ ├── initial_db.JPG │ └── relationship_model.JPG ├── intro_to_data_visualization_in_python │ ├── 1_1_axes.JPG │ ├── 1_3_annotate_options.JPG │ ├── 1_3_legend_locations.JPG │ ├── 2020-03-15_certificate.jpg │ ├── 2_1_2_array_orientation.JPG │ ├── 2_1_array_function.JPG │ ├── 2_1_array_image.JPG │ ├── 2_1_array_orientation.JPG │ ├── 2_1_meshgrid.jpg │ ├── 2_3_bin_shapes.JPG │ ├── 2_4_4_rescaling_pixel_intensities.JPG │ ├── 2_4_sunflower.jpg │ └── 4_3_low_contrast_mars_surface.JPG ├── intro_to_databases_in_python │ ├── 2019-03-23_intro_to_databases_in_python_certficate.JPG │ ├── columns_rows.JPG │ ├── hierarchical.JPG │ ├── related.JPG │ └── tables.JPG ├── joining_data_in_sql │ ├── 2019-12-12_certificate.jpg │ ├── anti_join.JPG │ ├── cross_join.JPG │ ├── except.JPG │ ├── full_join_1.JPG │ ├── full_join_2.JPG │ ├── inner_join_diagram.JPG │ ├── inner_join_diagram_small.JPG │ ├── intersect.JPG │ ├── left_join_diagram.JPG │ ├── left_join_multi_diagram.JPG │ ├── postgres_trimet.JPG │ ├── right_join_diagram.JPG │ ├── semi_join.JPG │ ├── union.JPG │ ├── union_all.JPG │ └── venn.JPG └── regex_example.JPG ├── Intermediate_Python.py ├── README.md ├── _config.yml ├── a_movie.json ├── case_study_hacker_statistics.py ├── data ├── 2019-12-13_intro_to_relational_databases │ └── university_professors.xlsx ├── 2020-03-15_interactive_data_visualization_with_bokeh │ └── state_coordinates.xlsx ├── 2020-06-01_statistical_thinking_2 │ └── nohitter_time.csv ├── 2021-03-29_unsupervised_learning_python │ └── artists_sparse.csv ├── AAPL.csv ├── airquality_dup.csv ├── billboard.csv ├── date_list.csv ├── df1_recognizing_tidy_data.csv ├── df2_recognizing_tidy_data.csv ├── ebola.csv ├── g1800s.csv ├── g1900s.csv ├── g2000s.csv ├── intro_to_python_for_finance │ └── exercise_data.csv ├── joining_data_in_sql │ ├── countries2 │ │ └── countries.sql │ ├── diagrams │ │ └── diagrams.sql │ └── leaders2 │ │ └── leaders.sql ├── manipulating-dataframes-with-pandas │ ├── LURReport.csv │ ├── auto-mpg.csv │ └── sales2.csv ├── merging-dataframes-with-pandas.7z ├── sales_data │ └── sales-feb-2015.csv ├── silso_sunspot_data_1818-2019.csv ├── stocks.7z ├── survey.csv ├── tb.csv ├── uber1.csv ├── uber2.csv └── uber3.csv ├── functions ├── multilabel.py ├── score_sub.py └── sparse_interactions.py ├── importing_data_in_python_1.py ├── importing_data_in_python_2.py ├── python_data_science_toolbox_1.py ├── python_data_science_toolbox_2.py ├── snakes.json ├── testing.py ├── tweets.csv └── twitter.py /.gitattributes: -------------------------------------------------------------------------------- 1 | # Auto detect text files and perform LF normalization 2 | * text=auto 3 | -------------------------------------------------------------------------------- /.gitignore: -------------------------------------------------------------------------------- 1 | .ipynb_checkpoints 2 | __pycache__/* 3 | .idea 4 | DataCamp-master/* 5 | data/*/*/*.sql 6 | data/*/*/*.pickle 7 | Images/*/gradient_descent_gif/* 8 | Images/*data_visualization*/480* 9 | Images/*data_visualization*/640* 10 | Images/*data_visualization*/axis_limits* 11 | Images/*data_visualization*/ps09* 12 | Images/*data_visualization*/sine_m* 13 | Images/*data_visualization*/xlim_and* 14 | data/*/archive 15 | 16 | *.csv 17 | *.sqlite 18 | *.pdf 19 | *.jpeg 20 | *.gif 21 | *.db3 22 | *.pkl 23 | *.url 24 | *.sas* 25 | *.zip 26 | *.sql 27 | /data 28 | -------------------------------------------------------------------------------- /.idea/other.xml: -------------------------------------------------------------------------------- 1 | 2 | 3 | 4 | 7 | -------------------------------------------------------------------------------- /DataCamp NB Template.ipynb: -------------------------------------------------------------------------------- 1 | { 2 | "cells": [ 3 | { 4 | "cell_type": "markdown", 5 | "metadata": {}, 6 | "source": [ 7 | "## Course\n", 8 | "\n", 9 | "- Notebook Author: [Trenton McKinney][1]\n", 10 | "- Course: **[DataCamp: Course Name][2]**\n", 11 | " - This [notebook][3] was created as a reproducible reference.\n", 12 | " - The material is from the course\n", 13 | " - The course website uses `scikit-learn v0.19.2`, `pandas v0.19.2`, and `numpy v1.17.4`\n", 14 | " - This notebook uses `v0.24.1`, `v1.2.3`, and `v1.19.2` respectively, so there are differences in model performance compared to the course.\n", 15 | " - I completed the exercises\n", 16 | " - If you find the content beneficial, consider a [DataCamp Subscription][4].\n", 17 | " - I added a function (**`create_dir_save_file`**) to automatically download and save the required data (`data/course_name`) and image (`Images/course_name`) files.\n", 18 | "\n", 19 | " [1]: https://trenton3983.github.io/\n", 20 | " [2]: https://Course_Link\n", 21 | " [3]: https://notebook_github_address\n", 22 | " [4]: https://www.datacamp.com/join-me/MjQwNzMyMg==" 23 | ] 24 | }, 25 | { 26 | "cell_type": "markdown", 27 | "metadata": {}, 28 | "source": [ 29 | "## Course Description\n", 30 | "\n", 31 | "Text" 32 | ] 33 | }, 34 | { 35 | "cell_type": "markdown", 36 | "metadata": {}, 37 | "source": [ 38 | "## Imports" 39 | ] 40 | }, 41 | { 42 | "cell_type": "code", 43 | "execution_count": null, 44 | "metadata": {}, 45 | "outputs": [], 46 | "source": [ 47 | "import pandas as pd\n", 48 | "from pprint import pprint as pp\n", 49 | "from itertools import combinations\n", 50 | "from pathlib import Path\n", 51 | "import requests\n", 52 | "import sys" 53 | ] 54 | }, 55 | { 56 | "cell_type": "markdown", 57 | "metadata": {}, 58 | "source": [ 59 | "## Configuration Options" 60 | ] 61 | }, 62 | { 63 | "cell_type": "code", 64 | "execution_count": null, 65 | "metadata": {}, 66 | "outputs": [], 67 | "source": [ 68 | "pd.set_option('max_columns', 200)\n", 69 | "pd.set_option('max_rows', 300)\n", 70 | "pd.set_option('display.expand_frame_repr', True)\n", 71 | "plt.rcParams[\"patch.force_edgecolor\"] = True" 72 | ] 73 | }, 74 | { 75 | "cell_type": "markdown", 76 | "metadata": {}, 77 | "source": [ 78 | "## Functions" 79 | ] 80 | }, 81 | { 82 | "cell_type": "code", 83 | "execution_count": null, 84 | "metadata": {}, 85 | "outputs": [], 86 | "source": [ 87 | "def create_dir_save_file(dir_path: Path, url: str):\n", 88 | " \"\"\"\n", 89 | " Check if the path exists and create it if it does not.\n", 90 | " Check if the file exists and download it if it does not.\n", 91 | " \"\"\"\n", 92 | " if not dir_path.parents[0].exists():\n", 93 | " dir_path.parents[0].mkdir(parents=True)\n", 94 | " print(f'Directory Created: {dir_path.parents[0]}')\n", 95 | " else:\n", 96 | " print('Directory Exists')\n", 97 | " \n", 98 | " if not dir_path.exists():\n", 99 | " r = requests.get(url, allow_redirects=True)\n", 100 | " open(dir_path, 'wb').write(r.content)\n", 101 | " print(f'File Created: {dir_path.name}')\n", 102 | " else:\n", 103 | " print('File Exists')" 104 | ] 105 | }, 106 | { 107 | "cell_type": "code", 108 | "execution_count": null, 109 | "metadata": {}, 110 | "outputs": [], 111 | "source": [ 112 | "data_dir = Path('data/...')\n", 113 | "images_dir = Path('Images/...')" 114 | ] 115 | }, 116 | { 117 | "cell_type": "markdown", 118 | "metadata": {}, 119 | "source": [ 120 | "## Datasets\n", 121 | "\n", 122 | "* [dataset][1]\n", 123 | "\n", 124 | " [1]: https://dataset" 125 | ] 126 | }, 127 | { 128 | "cell_type": "code", 129 | "execution_count": null, 130 | "metadata": {}, 131 | "outputs": [], 132 | "source": [ 133 | "file = 'https://...'" 134 | ] 135 | }, 136 | { 137 | "cell_type": "code", 138 | "execution_count": null, 139 | "metadata": {}, 140 | "outputs": [], 141 | "source": [ 142 | "datasets = [file]\n", 143 | "data_paths = list()\n", 144 | "\n", 145 | "for data in datasets:\n", 146 | " file_name = data.split('/')[-1].replace('?raw=true', '')\n", 147 | " data_path = data_dir / file_name\n", 148 | " create_dir_save_file(data_path, data)\n", 149 | " data_paths.append(data_path)" 150 | ] 151 | }, 152 | { 153 | "cell_type": "markdown", 154 | "metadata": {}, 155 | "source": [ 156 | "## DataFrames" 157 | ] 158 | }, 159 | { 160 | "cell_type": "code", 161 | "execution_count": null, 162 | "metadata": {}, 163 | "outputs": [], 164 | "source": [] 165 | }, 166 | { 167 | "cell_type": "markdown", 168 | "metadata": {}, 169 | "source": [ 170 | "## Memory Usage" 171 | ] 172 | }, 173 | { 174 | "cell_type": "code", 175 | "execution_count": null, 176 | "metadata": {}, 177 | "outputs": [], 178 | "source": [ 179 | "# These are the usual ipython objects, including this one you are creating\n", 180 | "ipython_vars = ['In', 'Out', 'exit', 'quit', 'get_ipython', 'ipython_vars'] # list a variables\n", 181 | "\n", 182 | "# Get a sorted list of the objects and their sizes\n", 183 | "sorted([(x, sys.getsizeof(globals().get(x))) for x in dir() if not x.startswith('_') and x not in sys.modules and x not in ipython_vars], key=lambda x: x[1], reverse=True)[:11]" 184 | ] 185 | }, 186 | { 187 | "cell_type": "markdown", 188 | "metadata": {}, 189 | "source": [ 190 | "# Section 1\n", 191 | "\n", 192 | "description" 193 | ] 194 | }, 195 | { 196 | "cell_type": "markdown", 197 | "metadata": {}, 198 | "source": [ 199 | "## Subsection Video Notes" 200 | ] 201 | }, 202 | { 203 | "cell_type": "markdown", 204 | "metadata": {}, 205 | "source": [ 206 | "### Questions" 207 | ] 208 | }, 209 | { 210 | "cell_type": "markdown", 211 | "metadata": {}, 212 | "source": [ 213 | "## Subsection Video Notes" 214 | ] 215 | }, 216 | { 217 | "cell_type": "markdown", 218 | "metadata": {}, 219 | "source": [ 220 | "### Questions" 221 | ] 222 | }, 223 | { 224 | "cell_type": "markdown", 225 | "metadata": {}, 226 | "source": [ 227 | "## Subsection Video Notes" 228 | ] 229 | }, 230 | { 231 | "cell_type": "markdown", 232 | "metadata": {}, 233 | "source": [ 234 | "### Questions" 235 | ] 236 | }, 237 | { 238 | "cell_type": "markdown", 239 | "metadata": {}, 240 | "source": [ 241 | "## Subsection Video Notes" 242 | ] 243 | }, 244 | { 245 | "cell_type": "markdown", 246 | "metadata": {}, 247 | "source": [ 248 | "### Questions" 249 | ] 250 | }, 251 | { 252 | "cell_type": "markdown", 253 | "metadata": {}, 254 | "source": [ 255 | "# Section 1\n", 256 | "\n", 257 | "description" 258 | ] 259 | }, 260 | { 261 | "cell_type": "markdown", 262 | "metadata": {}, 263 | "source": [ 264 | "## Subsection Video Notes" 265 | ] 266 | }, 267 | { 268 | "cell_type": "markdown", 269 | "metadata": {}, 270 | "source": [ 271 | "### Questions" 272 | ] 273 | }, 274 | { 275 | "cell_type": "markdown", 276 | "metadata": {}, 277 | "source": [ 278 | "## Subsection Video Notes" 279 | ] 280 | }, 281 | { 282 | "cell_type": "markdown", 283 | "metadata": {}, 284 | "source": [ 285 | "### Questions" 286 | ] 287 | }, 288 | { 289 | "cell_type": "markdown", 290 | "metadata": {}, 291 | "source": [ 292 | "## Subsection Video Notes" 293 | ] 294 | }, 295 | { 296 | "cell_type": "markdown", 297 | "metadata": {}, 298 | "source": [ 299 | "### Questions" 300 | ] 301 | }, 302 | { 303 | "cell_type": "markdown", 304 | "metadata": {}, 305 | "source": [ 306 | "## Subsection Video Notes" 307 | ] 308 | }, 309 | { 310 | "cell_type": "markdown", 311 | "metadata": {}, 312 | "source": [ 313 | "### Questions" 314 | ] 315 | }, 316 | { 317 | "cell_type": "markdown", 318 | "metadata": {}, 319 | "source": [ 320 | "# Section 1\n", 321 | "\n", 322 | "description" 323 | ] 324 | }, 325 | { 326 | "cell_type": "markdown", 327 | "metadata": {}, 328 | "source": [ 329 | "## Subsection Video Notes" 330 | ] 331 | }, 332 | { 333 | "cell_type": "markdown", 334 | "metadata": {}, 335 | "source": [ 336 | "### Questions" 337 | ] 338 | }, 339 | { 340 | "cell_type": "markdown", 341 | "metadata": {}, 342 | "source": [ 343 | "## Subsection Video Notes" 344 | ] 345 | }, 346 | { 347 | "cell_type": "markdown", 348 | "metadata": {}, 349 | "source": [ 350 | "### Questions" 351 | ] 352 | }, 353 | { 354 | "cell_type": "markdown", 355 | "metadata": {}, 356 | "source": [ 357 | "## Subsection Video Notes" 358 | ] 359 | }, 360 | { 361 | "cell_type": "markdown", 362 | "metadata": {}, 363 | "source": [ 364 | "### Questions" 365 | ] 366 | }, 367 | { 368 | "cell_type": "markdown", 369 | "metadata": {}, 370 | "source": [ 371 | "## Subsection Video Notes" 372 | ] 373 | }, 374 | { 375 | "cell_type": "markdown", 376 | "metadata": {}, 377 | "source": [ 378 | "### Questions" 379 | ] 380 | }, 381 | { 382 | "cell_type": "markdown", 383 | "metadata": {}, 384 | "source": [ 385 | "# Section 1\n", 386 | "\n", 387 | "description" 388 | ] 389 | }, 390 | { 391 | "cell_type": "markdown", 392 | "metadata": {}, 393 | "source": [ 394 | "## Subsection Video Notes" 395 | ] 396 | }, 397 | { 398 | "cell_type": "markdown", 399 | "metadata": {}, 400 | "source": [ 401 | "### Questions" 402 | ] 403 | }, 404 | { 405 | "cell_type": "markdown", 406 | "metadata": {}, 407 | "source": [ 408 | "## Subsection Video Notes" 409 | ] 410 | }, 411 | { 412 | "cell_type": "markdown", 413 | "metadata": {}, 414 | "source": [ 415 | "### Questions" 416 | ] 417 | }, 418 | { 419 | "cell_type": "markdown", 420 | "metadata": {}, 421 | "source": [ 422 | "## Subsection Video Notes" 423 | ] 424 | }, 425 | { 426 | "cell_type": "markdown", 427 | "metadata": {}, 428 | "source": [ 429 | "### Questions" 430 | ] 431 | }, 432 | { 433 | "cell_type": "markdown", 434 | "metadata": {}, 435 | "source": [ 436 | "## Subsection Video Notes" 437 | ] 438 | }, 439 | { 440 | "cell_type": "markdown", 441 | "metadata": {}, 442 | "source": [ 443 | "### Questions" 444 | ] 445 | }, 446 | { 447 | "cell_type": "markdown", 448 | "metadata": {}, 449 | "source": [ 450 | "# Certificate\n", 451 | "\n", 452 | "![](https://raw.githubusercontent.com/trenton3983/DataCamp/master/Images/jpd_dir/file.jpg)" 453 | ] 454 | } 455 | ], 456 | "metadata": { 457 | "kernelspec": { 458 | "display_name": "Python 3", 459 | "language": "python", 460 | "name": "python3" 461 | }, 462 | "language_info": { 463 | "codemirror_mode": { 464 | "name": "ipython", 465 | "version": 3 466 | }, 467 | "file_extension": ".py", 468 | "mimetype": "text/x-python", 469 | "name": "python", 470 | "nbconvert_exporter": "python", 471 | "pygments_lexer": "ipython3", 472 | "version": "3.8.8" 473 | }, 474 | "toc-autonumbering": true, 475 | "toc-showtags": false 476 | }, 477 | "nbformat": 4, 478 | "nbformat_minor": 4 479 | } 480 | -------------------------------------------------------------------------------- /Images/2019-02-04_manipulating_dataframes_with_pandas/2019-02-12_manipulating_dataframes_with_pandas_certificate.JPG: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/trenton3983/DataCamp/69fb59a5340f4d0340de24e1db305d859b6ae602/Images/2019-02-04_manipulating_dataframes_with_pandas/2019-02-12_manipulating_dataframes_with_pandas_certificate.JPG -------------------------------------------------------------------------------- /Images/2019-02-04_pandas_foundations/2019-02-04_pandas_foundations_certificate.jpg: 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-------------------------------------------------------------------------------- 1 | # DataCamp 2 | 3 | - DataCamp offers interactive courses in the browser. 4 | - I've taken coursers from COURSERA, edX, UDACITY, Udemy and LinkedIn Learning, but I prefer the format implemented by DataCamp. 5 | - As noted, the courses are interactive, in the browser, so I have taken the time to create Jupyter Notebooks for most of the courses I've taken. 6 | - I have implemented my notebooks in such a way as to be fully executable, though there may be some required backend setup (e.g. downloading data files or setting up a PostgreSQL database). 7 | - Links to data are in the notebook. 8 | - There are no hidden functions. 9 | - An executed HTML version of my notebooks can be found [here][1]. 10 | 11 | 12 | [1]: https://trenton3983.github.io/ 13 | -------------------------------------------------------------------------------- /_config.yml: -------------------------------------------------------------------------------- 1 | theme: jekyll-theme-cayman -------------------------------------------------------------------------------- /a_movie.json: -------------------------------------------------------------------------------- 1 | {"Actors": "Jesse Eisenberg, Rooney Mara, Bryan Barter, Dustin Fitzsimons", 2 | "Production": "Columbia Pictures", 3 | "Runtime": "120 min", 4 | "Plot": "Harvard student Mark Zuckerberg creates the social networking site that would become known as Facebook, but is later sued by two brothers who claimed he stole their idea, and the co-founder who was later squeezed out of the business.", 5 | "Writer": "Aaron Sorkin (screenplay), Ben Mezrich (book)", 6 | "Awards": "Won 3 Oscars. Another 165 wins & 168 nominations.", 7 | "Response": "True", 8 | "Type": "movie", 9 | "imdbID": "tt1285016", 10 | "Ratings": "[{'Source': 'Internet Movie Database', 'Value': '7.7/10'}, {'Source': 'Rotten Tomatoes', 'Value': '96%'}, {'Source': 'Metacritic', 'Value': '95/100'}]", 11 | "Genre": "Biography, Drama", 12 | "Released": "01 Oct 2010", 13 | "Director": "David Fincher", 14 | "Year": "2010", 15 | "Poster": "https://m.media-amazon.com/images/M/MV5BMTM2ODk0NDAwMF5BMl5BanBnXkFtZTcwNTM1MDc2Mw@@._V1_SX300.jpg", 16 | "BoxOffice": "$96,400,000", 17 | "imdbVotes": "542,658", 18 | "Website": "http://www.thesocialnetwork-movie.com/", 19 | "Rated": "PG-13", 20 | "imdbRating": "7.7", 21 | "DVD": "11 Jan 2011", 22 | "Title": "The Social Network", 23 | "Country": "USA", 24 | "Metascore": "95", 25 | "Language": "English, French"} -------------------------------------------------------------------------------- /case_study_hacker_statistics.py: -------------------------------------------------------------------------------- 1 | import numpy as np 2 | import matplotlib.pyplot as plt 3 | from pprint import pprint as pp 4 | 5 | 6 | def coin_toss(): 7 | np.random.seed(123) 8 | 9 | # np.random.rand() # float between 0 and 1 10 | 11 | coin = np.random.randint(0, 2) 12 | print(coin) 13 | if coin == 0: 14 | print('Heads') 15 | else: 16 | print('Tails') 17 | 18 | 19 | print('\nOutput from the function coin_toss:') 20 | coin_toss() 21 | 22 | 23 | def steps(): 24 | np.random.seed(123) 25 | 26 | # Starting step 27 | step = 50 28 | 29 | # Roll the dice 30 | dice = np.random.randint(1, 7) 31 | 32 | # Finish the control construct 33 | if dice <= 2: 34 | step = step - 1 35 | elif dice <= 5: 36 | step += 1 37 | else: 38 | step = step + np.random.randint(1, 7) 39 | 40 | # Print out dice and step 41 | print(dice) 42 | print(step) 43 | 44 | 45 | print('\nOutput from the function steps:') 46 | steps() 47 | 48 | 49 | def random_walk_fun(): 50 | np.random.seed(123) 51 | 52 | # Initialize random_walk 53 | random_walk = [0] 54 | 55 | # Complete the ___ 56 | for x in range(100): 57 | # Set step: last element in random_walk 58 | step = random_walk[-1] 59 | 60 | # Roll the dice 61 | dice = np.random.randint(1, 7) 62 | 63 | # Determine next step 64 | if dice <= 2: 65 | step = max(0, step - 1) 66 | elif dice <= 5: 67 | step = step + 1 68 | else: 69 | step = step + np.random.randint(1, 7) 70 | 71 | # append next_step to random_walk 72 | random_walk.append(step) 73 | 74 | # Print random_walk 75 | print(random_walk) 76 | 77 | # Plot random_walk 78 | plt.plot(random_walk) 79 | 80 | # Show the plot 81 | plt.show() 82 | 83 | 84 | print('\nOutput from the function random_walk_fun:') 85 | random_walk_fun() 86 | 87 | 88 | def mult_random_walk_fun(): 89 | np.random.seed(123) 90 | 91 | # Initialize all_walks 92 | all_walks = [] 93 | 94 | # Simulate random walk some number of times 95 | random_walks = 500 96 | for i in range(random_walks): 97 | 98 | # Code from before 99 | random_walk = [0] 100 | for x in range(100): 101 | step = random_walk[-1] 102 | dice = np.random.randint(1, 7) 103 | 104 | if dice <= 2: 105 | step = max(0, step - 1) 106 | elif dice <= 5: 107 | step = step + 1 108 | else: 109 | step = step + np.random.randint(1, 7) 110 | 111 | # Implement Clumsiness 112 | if np.random.rand() <= 0.001: 113 | step = 0 114 | 115 | random_walk.append(step) 116 | 117 | # Append random_walk to all_walks 118 | all_walks.append(random_walk) 119 | 120 | # Print all_walks 121 | print('All Walks') 122 | pp(all_walks, compact=True) 123 | 124 | # Convert all_walks to Numpy array: np_aw 125 | np_aw = np.array(all_walks) 126 | print('\nNP Array All Walks') 127 | pp(np_aw, compact=True) 128 | 129 | # Plot np_aw and show 130 | plt.plot(np_aw) 131 | plt.savefig('np_aw.png') 132 | plt.show() 133 | 134 | # Clear the figure 135 | plt.clf() 136 | 137 | # Transpose np_aw: np_aw_t 138 | np_aw_t = np.transpose(np_aw) 139 | print('\nNP Array All Walks Transposed') 140 | pp(np_aw_t, compact=True) 141 | 142 | # Plot np_aw_t and show 143 | plt.plot(np_aw_t) 144 | plt.savefig('np_aw_t.png') 145 | plt.show() 146 | plt.clf() 147 | 148 | # Select last row from np_aw_t: ends 149 | ends = np.array(np_aw_t[-1]) 150 | 151 | # Plot histogram of ends, display plot 152 | plt.hist(ends) 153 | plt.savefig('np_aw_t_last_hist.png') 154 | plt.show() 155 | 156 | # Calculate odds of reaching top of empire state building 157 | odds = (len(ends[ends >= 60])/random_walks)*100 158 | print(f'\nOdds of reaching the top of the Empire State Building based upon the results of {random_walks} walks is ' 159 | f'{odds}%') 160 | 161 | 162 | print('\nOutput from the function mult_random_walk_fun:') 163 | mult_random_walk_fun() 164 | -------------------------------------------------------------------------------- /data/2019-12-13_intro_to_relational_databases/university_professors.xlsx: 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545 13 | 715 14 | 966 15 | 624 16 | 29 17 | 450 18 | 107 19 | 20 20 | 91 21 | 1325 22 | 124 23 | 1468 24 | 104 25 | 1309 26 | 429 27 | 62 28 | 1878 29 | 1104 30 | 123 31 | 251 32 | 93 33 | 188 34 | 983 35 | 166 36 | 96 37 | 702 38 | 23 39 | 524 40 | 26 41 | 299 42 | 59 43 | 39 44 | 12 45 | 2 46 | 308 47 | 1114 48 | 813 49 | 887 50 | 645 51 | 2088 52 | 42 53 | 2090 54 | 11 55 | 886 56 | 1665 57 | 1084 58 | 2900 59 | 2432 60 | 750 61 | 4021 62 | 1070 63 | 1765 64 | 1322 65 | 26 66 | 548 67 | 1525 68 | 77 69 | 2181 70 | 2752 71 | 127 72 | 2147 73 | 211 74 | 41 75 | 1575 76 | 151 77 | 479 78 | 697 79 | 557 80 | 2267 81 | 542 82 | 392 83 | 73 84 | 603 85 | 233 86 | 255 87 | 528 88 | 397 89 | 1529 90 | 1023 91 | 1194 92 | 462 93 | 583 94 | 37 95 | 943 96 | 996 97 | 480 98 | 1497 99 | 717 100 | 224 101 | 219 102 | 1531 103 | 498 104 | 44 105 | 288 106 | 267 107 | 600 108 | 52 109 | 269 110 | 1086 111 | 386 112 | 176 113 | 2199 114 | 216 115 | 54 116 | 675 117 | 1243 118 | 463 119 | 650 120 | 171 121 | 327 122 | 110 123 | 774 124 | 509 125 | 8 126 | 197 127 | 136 128 | 12 129 | 1124 130 | 64 131 | 380 132 | 811 133 | 232 134 | 192 135 | 731 136 | 715 137 | 226 138 | 605 139 | 539 140 | 1491 141 | 323 142 | 240 143 | 179 144 | 702 145 | 156 146 | 82 147 | 1397 148 | 354 149 | 778 150 | 603 151 | 1001 152 | 385 153 | 986 154 | 203 155 | 149 156 | 576 157 | 445 158 | 180 159 | 1403 160 | 252 161 | 675 162 | 1351 163 | 2983 164 | 1568 165 | 45 166 | 899 167 | 3260 168 | 1025 169 | 31 170 | 100 171 | 2055 172 | 4043 173 | 79 174 | 238 175 | 3931 176 | 2351 177 | 595 178 | 110 179 | 215 180 | 0 181 | 563 182 | 206 183 | 660 184 | 242 185 | 577 186 | 179 187 | 157 188 | 192 189 | 192 190 | 1848 191 | 792 192 | 1693 193 | 55 194 | 388 195 | 225 196 | 1134 197 | 1172 198 | 1555 199 | 31 200 | 1582 201 | 1044 202 | 378 203 | 1687 204 | 2915 205 | 280 206 | 765 207 | 2819 208 | 511 209 | 1521 210 | 745 211 | 2491 212 | 580 213 | 2072 214 | 6450 215 | 578 216 | 745 217 | 1075 218 | 1103 219 | 1549 220 | 1520 221 | 138 222 | 1202 223 | 296 224 | 277 225 | 351 226 | 391 227 | 950 228 | 459 229 | 62 230 | 1056 231 | 1128 232 | 139 233 | 420 234 | 87 235 | 71 236 | 814 237 | 603 238 | 1349 239 | 162 240 | 1027 241 | 783 242 | 326 243 | 101 244 | 876 245 | 381 246 | 905 247 | 156 248 | 419 249 | 239 250 | 119 251 | 129 252 | 467 253 | -------------------------------------------------------------------------------- /data/df1_recognizing_tidy_data.csv: -------------------------------------------------------------------------------- 1 | Ozone,Solar.R,Wind,Temp,Month,Day 2 | 41,190,7.4,67,5,1 3 | 36,118,8,72,5,2 4 | 12,149,12.6,74,5,3 5 | 18,313,11.5,62,5,4 6 | NaN,NaN,14.3,56,5,5 7 | 28,NaN,14.9,66,5,6 8 | 23,299,8.6,65,5,7 9 | 19,99,13.8,59,5,8 10 | 8,19,20.1,61,5,9 11 | NaN,194,8.6,69,5,10 12 | 7,NaN,6.9,74,5,11 13 | 16,256,9.7,69,5,12 14 | 11,290,9.2,66,5,13 15 | 14,274,10.9,68,5,14 16 | 18,65,13.2,58,5,15 17 | 14,334,11.5,64,5,16 18 | 34,307,12,66,5,17 19 | 6,78,18.4,57,5,18 20 | 30,322,11.5,68,5,19 21 | 11,44,9.7,62,5,20 22 | 1,8,9.7,59,5,21 23 | 11,320,16.6,73,5,22 24 | 4,25,9.7,61,5,23 25 | 32,92,12,61,5,24 26 | NaN,66,16.6,57,5,25 27 | NaN,266,14.9,58,5,26 28 | NaN,NaN,8,57,5,27 29 | 23,13,12,67,5,28 30 | 45,252,14.9,81,5,29 31 | 115,223,5.7,79,5,30 32 | 37,279,7.4,76,5,31 33 | NaN,286,8.6,78,6,1 34 | NaN,287,9.7,74,6,2 35 | NaN,242,16.1,67,6,3 36 | NaN,186,9.2,84,6,4 37 | NaN,220,8.6,85,6,5 38 | NaN,264,14.3,79,6,6 39 | 29,127,9.7,82,6,7 40 | NaN,273,6.9,87,6,8 41 | 71,291,13.8,90,6,9 42 | 39,323,11.5,87,6,10 43 | NaN,259,10.9,93,6,11 44 | NaN,250,9.2,92,6,12 45 | 23,148,8,82,6,13 46 | NaN,332,13.8,80,6,14 47 | NaN,322,11.5,79,6,15 48 | 21,191,14.9,77,6,16 49 | 37,284,20.7,72,6,17 50 | 20,37,9.2,65,6,18 51 | 12,120,11.5,73,6,19 52 | 13,137,10.3,76,6,20 53 | NaN,150,6.3,77,6,21 54 | NaN,59,1.7,76,6,22 55 | NaN,91,4.6,76,6,23 56 | NaN,250,6.3,76,6,24 57 | NaN,135,8,75,6,25 58 | NaN,127,8,78,6,26 59 | NaN,47,10.3,73,6,27 60 | NaN,98,11.5,80,6,28 61 | NaN,31,14.9,77,6,29 62 | NaN,138,8,83,6,30 63 | 135,269,4.1,84,7,1 64 | 49,248,9.2,85,7,2 65 | 32,236,9.2,81,7,3 66 | NaN,101,10.9,84,7,4 67 | 64,175,4.6,83,7,5 68 | 40,314,10.9,83,7,6 69 | 77,276,5.1,88,7,7 70 | 97,267,6.3,92,7,8 71 | 97,272,5.7,92,7,9 72 | 85,175,7.4,89,7,10 73 | NaN,139,8.6,82,7,11 74 | 10,264,14.3,73,7,12 75 | 27,175,14.9,81,7,13 76 | NaN,291,14.9,91,7,14 77 | 7,48,14.3,80,7,15 78 | 48,260,6.9,81,7,16 79 | 35,274,10.3,82,7,17 80 | 61,285,6.3,84,7,18 81 | 79,187,5.1,87,7,19 82 | 63,220,11.5,85,7,20 83 | 16,7,6.9,74,7,21 84 | NaN,258,9.7,81,7,22 85 | NaN,295,11.5,82,7,23 86 | 80,294,8.6,86,7,24 87 | 108,223,8,85,7,25 88 | 20,81,8.6,82,7,26 89 | 52,82,12,86,7,27 90 | 82,213,7.4,88,7,28 91 | 50,275,7.4,86,7,29 92 | 64,253,7.4,83,7,30 93 | 59,254,9.2,81,7,31 94 | 39,83,6.9,81,8,1 95 | 9,24,13.8,81,8,2 96 | 16,77,7.4,82,8,3 97 | 78,NaN,6.9,86,8,4 98 | 35,NaN,7.4,85,8,5 99 | 66,NaN,4.6,87,8,6 100 | 122,255,4,89,8,7 101 | 89,229,10.3,90,8,8 102 | 110,207,8,90,8,9 103 | NaN,222,8.6,92,8,10 104 | NaN,137,11.5,86,8,11 105 | 44,192,11.5,86,8,12 106 | 28,273,11.5,82,8,13 107 | 65,157,9.7,80,8,14 108 | NaN,64,11.5,79,8,15 109 | 22,71,10.3,77,8,16 110 | 59,51,6.3,79,8,17 111 | 23,115,7.4,76,8,18 112 | 31,244,10.9,78,8,19 113 | 44,190,10.3,78,8,20 114 | 21,259,15.5,77,8,21 115 | 9,36,14.3,72,8,22 116 | NaN,255,12.6,75,8,23 117 | 45,212,9.7,79,8,24 118 | 168,238,3.4,81,8,25 119 | 73,215,8,86,8,26 120 | NaN,153,5.7,88,8,27 121 | 76,203,9.7,97,8,28 122 | 118,225,2.3,94,8,29 123 | 84,237,6.3,96,8,30 124 | 85,188,6.3,94,8,31 125 | 96,167,6.9,91,9,1 126 | 78,197,5.1,92,9,2 127 | 73,183,2.8,93,9,3 128 | 91,189,4.6,93,9,4 129 | 47,95,7.4,87,9,5 130 | 32,92,15.5,84,9,6 131 | 20,252,10.9,80,9,7 132 | 23,220,10.3,78,9,8 133 | 21,230,10.9,75,9,9 134 | 24,259,9.7,73,9,10 135 | 44,236,14.9,81,9,11 136 | 21,259,15.5,76,9,12 137 | 28,238,6.3,77,9,13 138 | 9,24,10.9,71,9,14 139 | 13,112,11.5,71,9,15 140 | 46,237,6.9,78,9,16 141 | 18,224,13.8,67,9,17 142 | 13,27,10.3,76,9,18 143 | 24,238,10.3,68,9,19 144 | 16,201,8,82,9,20 145 | 13,238,12.6,64,9,21 146 | 23,14,9.2,71,9,22 147 | 36,139,10.3,81,9,23 148 | 7,49,10.3,69,9,24 149 | 14,20,16.6,63,9,25 150 | 30,193,6.9,70,9,26 151 | NaN,145,13.2,77,9,27 152 | 14,191,14.3,75,9,28 153 | 18,131,8,76,9,29 154 | 20,223,11.5,68,9,30 155 | -------------------------------------------------------------------------------- /data/df2_recognizing_tidy_data.csv: -------------------------------------------------------------------------------- 1 | Month,Day,variable,value 2 | 5,1,Ozone,41 3 | 5,1,Solar.R,190 4 | 5,1,Wind,7.4 5 | 5,1,Temp,67 6 | 5,2,Ozone,36 7 | 5,2,Solar.R,118 8 | 5,2,Wind,8 9 | 5,2,Temp,72 10 | 5,3,Ozone,12 11 | 5,3,Solar.R,149 12 | 5,3,Wind,12.6 13 | 5,3,Temp,74 14 | 5,4,Ozone,18 15 | 5,4,Solar.R,313 16 | 5,4,Wind,11.5 17 | 5,4,Temp,62 18 | 5,5,Ozone,#NUM! 19 | 5,5,Solar.R,#NUM! 20 | 5,5,Wind,14.3 21 | 5,5,Temp,56 22 | 5,6,Ozone,28 23 | 5,6,Solar.R,#NUM! 24 | 5,6,Wind,14.9 25 | 5,6,Temp,66 26 | 5,7,Ozone,23 27 | 5,7,Solar.R,299 28 | 5,7,Wind,8.6 29 | 5,7,Temp,65 30 | 5,8,Ozone,19 31 | 5,8,Solar.R,99 32 | 5,8,Wind,13.8 33 | 5,8,Temp,59 34 | 5,9,Ozone,8 35 | 5,9,Solar.R,19 36 | 5,9,Wind,20.1 37 | 5,9,Temp,61 38 | 5,10,Ozone,#NUM! 39 | 5,10,Solar.R,194 40 | 5,10,Wind,8.6 41 | 5,10,Temp,69 42 | 5,11,Ozone,7 43 | 5,11,Solar.R,#NUM! 44 | 5,11,Wind,6.9 45 | 5,11,Temp,74 46 | 5,12,Ozone,16 47 | 5,12,Solar.R,256 48 | 5,12,Wind,9.7 49 | 5,12,Temp,69 50 | 5,13,Ozone,11 51 | 5,13,Solar.R,290 52 | 5,13,Wind,9.2 53 | 5,13,Temp,66 54 | 5,14,Ozone,14 55 | 5,14,Solar.R,274 56 | 5,14,Wind,10.9 57 | 5,14,Temp,68 58 | 5,15,Ozone,18 59 | 5,15,Solar.R,65 60 | 5,15,Wind,13.2 61 | 5,15,Temp,58 62 | 5,16,Ozone,14 63 | 5,16,Solar.R,334 64 | 5,16,Wind,11.5 65 | 5,16,Temp,64 66 | 5,17,Ozone,34 67 | 5,17,Solar.R,307 68 | 5,17,Wind,12 69 | 5,17,Temp,66 70 | 5,18,Ozone,6 71 | 5,18,Solar.R,78 72 | 5,18,Wind,18.4 73 | 5,18,Temp,57 74 | 5,19,Ozone,30 75 | 5,19,Solar.R,322 76 | 5,19,Wind,11.5 77 | 5,19,Temp,68 78 | 5,20,Ozone,11 79 | 5,20,Solar.R,44 80 | 5,20,Wind,9.7 81 | 5,20,Temp,62 82 | 5,21,Ozone,1 83 | 5,21,Solar.R,8 84 | 5,21,Wind,9.7 85 | 5,21,Temp,59 86 | 5,22,Ozone,11 87 | 5,22,Solar.R,320 88 | 5,22,Wind,16.6 89 | 5,22,Temp,73 90 | 5,23,Ozone,4 91 | 5,23,Solar.R,25 92 | 5,23,Wind,9.7 93 | 5,23,Temp,61 94 | 5,24,Ozone,32 95 | 5,24,Solar.R,92 96 | 5,24,Wind,12 97 | 5,24,Temp,61 98 | 5,25,Ozone,#NUM! 99 | 5,25,Solar.R,66 100 | 5,25,Wind,16.6 101 | 5,25,Temp,57 102 | 5,26,Ozone,#NUM! 103 | 5,26,Solar.R,266 104 | 5,26,Wind,14.9 105 | 5,26,Temp,58 106 | 5,27,Ozone,#NUM! 107 | 5,27,Solar.R,#NUM! 108 | 5,27,Wind,8 109 | 5,27,Temp,57 110 | 5,28,Ozone,23 111 | 5,28,Solar.R,13 112 | 5,28,Wind,12 113 | 5,28,Temp,67 114 | 5,29,Ozone,45 115 | 5,29,Solar.R,252 116 | 5,29,Wind,14.9 117 | 5,29,Temp,81 118 | 5,30,Ozone,115 119 | 5,30,Solar.R,223 120 | 5,30,Wind,5.7 121 | 5,30,Temp,79 122 | 5,31,Ozone,37 123 | 5,31,Solar.R,279 124 | 5,31,Wind,7.4 125 | 5,31,Temp,76 126 | 6,1,Ozone,#NUM! 127 | 6,1,Solar.R,286 128 | 6,1,Wind,8.6 129 | 6,1,Temp,78 130 | 6,2,Ozone,#NUM! 131 | 6,2,Solar.R,287 132 | 6,2,Wind,9.7 133 | 6,2,Temp,74 134 | 6,3,Ozone,#NUM! 135 | 6,3,Solar.R,242 136 | 6,3,Wind,16.1 137 | 6,3,Temp,67 138 | 6,4,Ozone,#NUM! 139 | 6,4,Solar.R,186 140 | 6,4,Wind,9.2 141 | 6,4,Temp,84 142 | 6,5,Ozone,#NUM! 143 | 6,5,Solar.R,220 144 | 6,5,Wind,8.6 145 | 6,5,Temp,85 146 | 6,6,Ozone,#NUM! 147 | 6,6,Solar.R,264 148 | 6,6,Wind,14.3 149 | 6,6,Temp,79 150 | 6,7,Ozone,29 151 | 6,7,Solar.R,127 152 | 6,7,Wind,9.7 153 | 6,7,Temp,82 154 | 6,8,Ozone,#NUM! 155 | 6,8,Solar.R,273 156 | 6,8,Wind,6.9 157 | 6,8,Temp,87 158 | 6,9,Ozone,71 159 | 6,9,Solar.R,291 160 | 6,9,Wind,13.8 161 | 6,9,Temp,90 162 | 6,10,Ozone,39 163 | 6,10,Solar.R,323 164 | 6,10,Wind,11.5 165 | 6,10,Temp,87 166 | 6,11,Ozone,#NUM! 167 | 6,11,Solar.R,259 168 | 6,11,Wind,10.9 169 | 6,11,Temp,93 170 | 6,12,Ozone,#NUM! 171 | 6,12,Solar.R,250 172 | 6,12,Wind,9.2 173 | 6,12,Temp,92 174 | 6,13,Ozone,23 175 | 6,13,Solar.R,148 176 | 6,13,Wind,8 177 | 6,13,Temp,82 178 | 6,14,Ozone,#NUM! 179 | 6,14,Solar.R,332 180 | 6,14,Wind,13.8 181 | 6,14,Temp,80 182 | 6,15,Ozone,#NUM! 183 | 6,15,Solar.R,322 184 | 6,15,Wind,11.5 185 | 6,15,Temp,79 186 | 6,16,Ozone,21 187 | 6,16,Solar.R,191 188 | 6,16,Wind,14.9 189 | 6,16,Temp,77 190 | 6,17,Ozone,37 191 | 6,17,Solar.R,284 192 | 6,17,Wind,20.7 193 | 6,17,Temp,72 194 | 6,18,Ozone,20 195 | 6,18,Solar.R,37 196 | 6,18,Wind,9.2 197 | 6,18,Temp,65 198 | 6,19,Ozone,12 199 | 6,19,Solar.R,120 200 | 6,19,Wind,11.5 201 | 6,19,Temp,73 202 | 6,20,Ozone,13 203 | 6,20,Solar.R,137 204 | 6,20,Wind,10.3 205 | 6,20,Temp,76 206 | 6,21,Ozone,#NUM! 207 | 6,21,Solar.R,150 208 | 6,21,Wind,6.3 209 | 6,21,Temp,77 210 | 6,22,Ozone,#NUM! 211 | 6,22,Solar.R,59 212 | 6,22,Wind,1.7 213 | 6,22,Temp,76 214 | 6,23,Ozone,#NUM! 215 | 6,23,Solar.R,91 216 | 6,23,Wind,4.6 217 | 6,23,Temp,76 218 | 6,24,Ozone,#NUM! 219 | 6,24,Solar.R,250 220 | 6,24,Wind,6.3 221 | 6,24,Temp,76 222 | 6,25,Ozone,#NUM! 223 | 6,25,Solar.R,135 224 | 6,25,Wind,8 225 | 6,25,Temp,75 226 | 6,26,Ozone,#NUM! 227 | 6,26,Solar.R,127 228 | 6,26,Wind,8 229 | 6,26,Temp,78 230 | 6,27,Ozone,#NUM! 231 | 6,27,Solar.R,47 232 | 6,27,Wind,10.3 233 | 6,27,Temp,73 234 | 6,28,Ozone,#NUM! 235 | 6,28,Solar.R,98 236 | 6,28,Wind,11.5 237 | 6,28,Temp,80 238 | 6,29,Ozone,#NUM! 239 | 6,29,Solar.R,31 240 | 6,29,Wind,14.9 241 | 6,29,Temp,77 242 | 6,30,Ozone,#NUM! 243 | 6,30,Solar.R,138 244 | 6,30,Wind,8 245 | 6,30,Temp,83 246 | 7,1,Ozone,135 247 | 7,1,Solar.R,269 248 | 7,1,Wind,4.1 249 | 7,1,Temp,84 250 | 7,2,Ozone,49 251 | 7,2,Solar.R,248 252 | 7,2,Wind,9.2 253 | 7,2,Temp,85 254 | 7,3,Ozone,32 255 | 7,3,Solar.R,236 256 | 7,3,Wind,9.2 257 | 7,3,Temp,81 258 | 7,4,Ozone,#NUM! 259 | 7,4,Solar.R,101 260 | 7,4,Wind,10.9 261 | 7,4,Temp,84 262 | 7,5,Ozone,64 263 | 7,5,Solar.R,175 264 | 7,5,Wind,4.6 265 | 7,5,Temp,83 266 | 7,6,Ozone,40 267 | 7,6,Solar.R,314 268 | 7,6,Wind,10.9 269 | 7,6,Temp,83 270 | 7,7,Ozone,77 271 | 7,7,Solar.R,276 272 | 7,7,Wind,5.1 273 | 7,7,Temp,88 274 | 7,8,Ozone,97 275 | 7,8,Solar.R,267 276 | 7,8,Wind,6.3 277 | 7,8,Temp,92 278 | 7,9,Ozone,97 279 | 7,9,Solar.R,272 280 | 7,9,Wind,5.7 281 | 7,9,Temp,92 282 | 7,10,Ozone,85 283 | 7,10,Solar.R,175 284 | 7,10,Wind,7.4 285 | 7,10,Temp,89 286 | 7,11,Ozone,#NUM! 287 | 7,11,Solar.R,139 288 | 7,11,Wind,8.6 289 | 7,11,Temp,82 290 | 7,12,Ozone,10 291 | 7,12,Solar.R,264 292 | 7,12,Wind,14.3 293 | 7,12,Temp,73 294 | 7,13,Ozone,27 295 | 7,13,Solar.R,175 296 | 7,13,Wind,14.9 297 | 7,13,Temp,81 298 | 7,14,Ozone,#NUM! 299 | 7,14,Solar.R,291 300 | 7,14,Wind,14.9 301 | 7,14,Temp,91 302 | 7,15,Ozone,7 303 | 7,15,Solar.R,48 304 | 7,15,Wind,14.3 305 | 7,15,Temp,80 306 | 7,16,Ozone,48 307 | 7,16,Solar.R,260 308 | 7,16,Wind,6.9 309 | 7,16,Temp,81 310 | 7,17,Ozone,35 311 | 7,17,Solar.R,274 312 | 7,17,Wind,10.3 313 | 7,17,Temp,82 314 | 7,18,Ozone,61 315 | 7,18,Solar.R,285 316 | 7,18,Wind,6.3 317 | 7,18,Temp,84 318 | 7,19,Ozone,79 319 | 7,19,Solar.R,187 320 | 7,19,Wind,5.1 321 | 7,19,Temp,87 322 | 7,20,Ozone,63 323 | 7,20,Solar.R,220 324 | 7,20,Wind,11.5 325 | 7,20,Temp,85 326 | 7,21,Ozone,16 327 | 7,21,Solar.R,7 328 | 7,21,Wind,6.9 329 | 7,21,Temp,74 330 | 7,22,Ozone,#NUM! 331 | 7,22,Solar.R,258 332 | 7,22,Wind,9.7 333 | 7,22,Temp,81 334 | 7,23,Ozone,#NUM! 335 | 7,23,Solar.R,295 336 | 7,23,Wind,11.5 337 | 7,23,Temp,82 338 | 7,24,Ozone,80 339 | 7,24,Solar.R,294 340 | 7,24,Wind,8.6 341 | 7,24,Temp,86 342 | 7,25,Ozone,108 343 | 7,25,Solar.R,223 344 | 7,25,Wind,8 345 | 7,25,Temp,85 346 | 7,26,Ozone,20 347 | 7,26,Solar.R,81 348 | 7,26,Wind,8.6 349 | 7,26,Temp,82 350 | 7,27,Ozone,52 351 | 7,27,Solar.R,82 352 | 7,27,Wind,12 353 | 7,27,Temp,86 354 | 7,28,Ozone,82 355 | 7,28,Solar.R,213 356 | 7,28,Wind,7.4 357 | 7,28,Temp,88 358 | 7,29,Ozone,50 359 | 7,29,Solar.R,275 360 | 7,29,Wind,7.4 361 | 7,29,Temp,86 362 | 7,30,Ozone,64 363 | 7,30,Solar.R,253 364 | 7,30,Wind,7.4 365 | 7,30,Temp,83 366 | 7,31,Ozone,59 367 | 7,31,Solar.R,254 368 | 7,31,Wind,9.2 369 | 7,31,Temp,81 370 | 8,1,Ozone,39 371 | 8,1,Solar.R,83 372 | 8,1,Wind,6.9 373 | 8,1,Temp,81 374 | 8,2,Ozone,9 375 | 8,2,Solar.R,24 376 | 8,2,Wind,13.8 377 | 8,2,Temp,81 378 | 8,3,Ozone,16 379 | 8,3,Solar.R,77 380 | 8,3,Wind,7.4 381 | 8,3,Temp,82 382 | 8,4,Ozone,78 383 | 8,4,Solar.R,#NUM! 384 | 8,4,Wind,6.9 385 | 8,4,Temp,86 386 | 8,5,Ozone,35 387 | 8,5,Solar.R,#NUM! 388 | 8,5,Wind,7.4 389 | 8,5,Temp,85 390 | 8,6,Ozone,66 391 | 8,6,Solar.R,#NUM! 392 | 8,6,Wind,4.6 393 | 8,6,Temp,87 394 | 8,7,Ozone,122 395 | 8,7,Solar.R,255 396 | 8,7,Wind,4 397 | 8,7,Temp,89 398 | 8,8,Ozone,89 399 | 8,8,Solar.R,229 400 | 8,8,Wind,10.3 401 | 8,8,Temp,90 402 | 8,9,Ozone,110 403 | 8,9,Solar.R,207 404 | 8,9,Wind,8 405 | 8,9,Temp,90 406 | 8,10,Ozone,#NUM! 407 | 8,10,Solar.R,222 408 | 8,10,Wind,8.6 409 | 8,10,Temp,92 410 | 8,11,Ozone,#NUM! 411 | 8,11,Solar.R,137 412 | 8,11,Wind,11.5 413 | 8,11,Temp,86 414 | 8,12,Ozone,44 415 | 8,12,Solar.R,192 416 | 8,12,Wind,11.5 417 | 8,12,Temp,86 418 | 8,13,Ozone,28 419 | 8,13,Solar.R,273 420 | 8,13,Wind,11.5 421 | 8,13,Temp,82 422 | 8,14,Ozone,65 423 | 8,14,Solar.R,157 424 | 8,14,Wind,9.7 425 | 8,14,Temp,80 426 | 8,15,Ozone,#NUM! 427 | 8,15,Solar.R,64 428 | 8,15,Wind,11.5 429 | 8,15,Temp,79 430 | 8,16,Ozone,22 431 | 8,16,Solar.R,71 432 | 8,16,Wind,10.3 433 | 8,16,Temp,77 434 | 8,17,Ozone,59 435 | 8,17,Solar.R,51 436 | 8,17,Wind,6.3 437 | 8,17,Temp,79 438 | 8,18,Ozone,23 439 | 8,18,Solar.R,115 440 | 8,18,Wind,7.4 441 | 8,18,Temp,76 442 | 8,19,Ozone,31 443 | 8,19,Solar.R,244 444 | 8,19,Wind,10.9 445 | 8,19,Temp,78 446 | 8,20,Ozone,44 447 | 8,20,Solar.R,190 448 | 8,20,Wind,10.3 449 | 8,20,Temp,78 450 | 8,21,Ozone,21 451 | 8,21,Solar.R,259 452 | 8,21,Wind,15.5 453 | 8,21,Temp,77 454 | 8,22,Ozone,9 455 | 8,22,Solar.R,36 456 | 8,22,Wind,14.3 457 | 8,22,Temp,72 458 | 8,23,Ozone,#NUM! 459 | 8,23,Solar.R,255 460 | 8,23,Wind,12.6 461 | 8,23,Temp,75 462 | 8,24,Ozone,45 463 | 8,24,Solar.R,212 464 | 8,24,Wind,9.7 465 | 8,24,Temp,79 466 | 8,25,Ozone,168 467 | 8,25,Solar.R,238 468 | 8,25,Wind,3.4 469 | 8,25,Temp,81 470 | 8,26,Ozone,73 471 | 8,26,Solar.R,215 472 | 8,26,Wind,8 473 | 8,26,Temp,86 474 | 8,27,Ozone,#NUM! 475 | 8,27,Solar.R,153 476 | 8,27,Wind,5.7 477 | 8,27,Temp,88 478 | 8,28,Ozone,76 479 | 8,28,Solar.R,203 480 | 8,28,Wind,9.7 481 | 8,28,Temp,97 482 | 8,29,Ozone,118 483 | 8,29,Solar.R,225 484 | 8,29,Wind,2.3 485 | 8,29,Temp,94 486 | 8,30,Ozone,84 487 | 8,30,Solar.R,237 488 | 8,30,Wind,6.3 489 | 8,30,Temp,96 490 | 8,31,Ozone,85 491 | 8,31,Solar.R,188 492 | 8,31,Wind,6.3 493 | 8,31,Temp,94 494 | 9,1,Ozone,96 495 | 9,1,Solar.R,167 496 | 9,1,Wind,6.9 497 | 9,1,Temp,91 498 | 9,2,Ozone,78 499 | 9,2,Solar.R,197 500 | 9,2,Wind,5.1 501 | 9,2,Temp,92 502 | 9,3,Ozone,73 503 | 9,3,Solar.R,183 504 | 9,3,Wind,2.8 505 | 9,3,Temp,93 506 | 9,4,Ozone,91 507 | 9,4,Solar.R,189 508 | 9,4,Wind,4.6 509 | 9,4,Temp,93 510 | 9,5,Ozone,47 511 | 9,5,Solar.R,95 512 | 9,5,Wind,7.4 513 | 9,5,Temp,87 514 | 9,6,Ozone,32 515 | 9,6,Solar.R,92 516 | 9,6,Wind,15.5 517 | 9,6,Temp,84 518 | 9,7,Ozone,20 519 | 9,7,Solar.R,252 520 | 9,7,Wind,10.9 521 | 9,7,Temp,80 522 | 9,8,Ozone,23 523 | 9,8,Solar.R,220 524 | 9,8,Wind,10.3 525 | 9,8,Temp,78 526 | 9,9,Ozone,21 527 | 9,9,Solar.R,230 528 | 9,9,Wind,10.9 529 | 9,9,Temp,75 530 | 9,10,Ozone,24 531 | 9,10,Solar.R,259 532 | 9,10,Wind,9.7 533 | 9,10,Temp,73 534 | 9,11,Ozone,44 535 | 9,11,Solar.R,236 536 | 9,11,Wind,14.9 537 | 9,11,Temp,81 538 | 9,12,Ozone,21 539 | 9,12,Solar.R,259 540 | 9,12,Wind,15.5 541 | 9,12,Temp,76 542 | 9,13,Ozone,28 543 | 9,13,Solar.R,238 544 | 9,13,Wind,6.3 545 | 9,13,Temp,77 546 | 9,14,Ozone,9 547 | 9,14,Solar.R,24 548 | 9,14,Wind,10.9 549 | 9,14,Temp,71 550 | 9,15,Ozone,13 551 | 9,15,Solar.R,112 552 | 9,15,Wind,11.5 553 | 9,15,Temp,71 554 | 9,16,Ozone,46 555 | 9,16,Solar.R,237 556 | 9,16,Wind,6.9 557 | 9,16,Temp,78 558 | 9,17,Ozone,18 559 | 9,17,Solar.R,224 560 | 9,17,Wind,13.8 561 | 9,17,Temp,67 562 | 9,18,Ozone,13 563 | 9,18,Solar.R,27 564 | 9,18,Wind,10.3 565 | 9,18,Temp,76 566 | 9,19,Ozone,24 567 | 9,19,Solar.R,238 568 | 9,19,Wind,10.3 569 | 9,19,Temp,68 570 | 9,20,Ozone,16 571 | 9,20,Solar.R,201 572 | 9,20,Wind,8 573 | 9,20,Temp,82 574 | 9,21,Ozone,13 575 | 9,21,Solar.R,238 576 | 9,21,Wind,12.6 577 | 9,21,Temp,64 578 | 9,22,Ozone,23 579 | 9,22,Solar.R,14 580 | 9,22,Wind,9.2 581 | 9,22,Temp,71 582 | 9,23,Ozone,36 583 | 9,23,Solar.R,139 584 | 9,23,Wind,10.3 585 | 9,23,Temp,81 586 | 9,24,Ozone,7 587 | 9,24,Solar.R,49 588 | 9,24,Wind,10.3 589 | 9,24,Temp,69 590 | 9,25,Ozone,14 591 | 9,25,Solar.R,20 592 | 9,25,Wind,16.6 593 | 9,25,Temp,63 594 | 9,26,Ozone,30 595 | 9,26,Solar.R,193 596 | 9,26,Wind,6.9 597 | 9,26,Temp,70 598 | 9,27,Ozone,#NUM! 599 | 9,27,Solar.R,145 600 | 9,27,Wind,13.2 601 | 9,27,Temp,77 602 | 9,28,Ozone,14 603 | 9,28,Solar.R,191 604 | 9,28,Wind,14.3 605 | 9,28,Temp,75 606 | 9,29,Ozone,18 607 | 9,29,Solar.R,131 608 | 9,29,Wind,8 609 | 9,29,Temp,76 610 | 9,30,Ozone,20 611 | 9,30,Solar.R,223 612 | 9,30,Wind,11.5 613 | 9,30,Temp,68 614 | -------------------------------------------------------------------------------- /data/ebola.csv: -------------------------------------------------------------------------------- 1 | Date,Day,Cases_Guinea,Cases_Liberia,Cases_SierraLeone,Cases_Nigeria,Cases_Senegal,Cases_UnitedStates,Cases_Spain,Cases_Mali,Deaths_Guinea,Deaths_Liberia,Deaths_SierraLeone,Deaths_Nigeria,Deaths_Senegal,Deaths_UnitedStates,Deaths_Spain,Deaths_Mali 2 | 1/5/2015,289,2776,NaN,10030,NaN,NaN,NaN,NaN,NaN,1786,NaN,2977,NaN,NaN,NaN,NaN,NaN 3 | 1/4/2015,288,2775,NaN,9780,NaN,NaN,NaN,NaN,NaN,1781,NaN,2943,NaN,NaN,NaN,NaN,NaN 4 | 1/3/2015,287,2769,8166,9722,NaN,NaN,NaN,NaN,NaN,1767,3496,2915,NaN,NaN,NaN,NaN,NaN 5 | 1/2/2015,286,NaN,8157,NaN,NaN,NaN,NaN,NaN,NaN,NaN,3496,NaN,NaN,NaN,NaN,NaN,NaN 6 | 12/31/2014,284,2730,8115,9633,NaN,NaN,NaN,NaN,NaN,1739,3471,2827,NaN,NaN,NaN,NaN,NaN 7 | 12/28/2014,281,2706,8018,9446,NaN,NaN,NaN,NaN,NaN,1708,3423,2758,NaN,NaN,NaN,NaN,NaN 8 | 12/27/2014,280,2695,NaN,9409,NaN,NaN,NaN,NaN,NaN,1697,NaN,2732,NaN,NaN,NaN,NaN,NaN 9 | 12/24/2014,277,2630,7977,9203,NaN,NaN,NaN,NaN,NaN,NaN,3413,2655,NaN,NaN,NaN,NaN,NaN 10 | 12/21/2014,273,2597,NaN,9004,NaN,NaN,NaN,NaN,NaN,1607,NaN,2582,NaN,NaN,NaN,NaN,NaN 11 | 12/20/2014,272,2571,7862,8939,NaN,NaN,NaN,NaN,NaN,1586,3384,2556,NaN,NaN,NaN,NaN,NaN 12 | 12/18/2014,271,NaN,7830,NaN,NaN,NaN,NaN,NaN,NaN,NaN,3376,NaN,NaN,NaN,NaN,NaN,NaN 13 | 12/14/2014,267,2416,NaN,8356,NaN,NaN,NaN,NaN,NaN,1525,NaN,2085,NaN,NaN,NaN,NaN,NaN 14 | 12/9/2014,262,NaN,7797,NaN,NaN,NaN,NaN,NaN,NaN,NaN,3290,NaN,NaN,NaN,NaN,NaN,NaN 15 | 12/7/2014,260,2292,NaN,7897,20,1,4,1,7,1428,NaN,1768,8,0,1,0,6 16 | 12/3/2014,256,NaN,7719,NaN,NaN,NaN,NaN,NaN,NaN,NaN,3177,NaN,NaN,NaN,NaN,NaN,NaN 17 | 11/30/2014,253,2164,NaN,7312,20,1,4,1,7,1327,NaN,1583,8,0,1,0,6 18 | 11/28/2014,251,NaN,7635,NaN,NaN,NaN,NaN,NaN,NaN,NaN,3145,NaN,NaN,NaN,NaN,NaN,NaN 19 | 11/23/2014,246,2134,NaN,6599,20,1,4,1,7,1260,NaN,1398,8,0,1,0,6 20 | 11/22/2014,245,NaN,7168,NaN,NaN,NaN,NaN,NaN,NaN,NaN,3016,NaN,NaN,NaN,NaN,NaN,NaN 21 | 11/18/2014,241,2047,7082,6190,20,1,4,1,6,1214,2963,1267,8,0,1,0,6 22 | 11/16/2014,239,1971,NaN,6073,20,1,4,1,5,1192,NaN,1250,8,0,1,0,5 23 | 11/15/2014,238,NaN,7069,NaN,NaN,NaN,NaN,NaN,NaN,NaN,2964,NaN,NaN,NaN,NaN,NaN,NaN 24 | 11/11/2014,234,1919,NaN,5586,20,1,4,1,4,1166,NaN,1187,8,0,1,0,3 25 | 11/10/2014,233,NaN,6878,NaN,NaN,NaN,NaN,NaN,NaN,NaN,2812,NaN,NaN,NaN,NaN,NaN,NaN 26 | 11/9/2014,232,1878,NaN,5368,20,1,4,1,1,1142,NaN,1169,8,0,1,0,1 27 | 11/8/2014,231,NaN,6822,NaN,NaN,NaN,NaN,NaN,NaN,NaN,2836,NaN,NaN,NaN,NaN,NaN,NaN 28 | 11/4/2014,227,NaN,6619,4862,20,1,4,1,1,NaN,2766,1130,8,0,1,0,1 29 | 11/3/2014,226,1760,NaN,NaN,NaN,NaN,NaN,NaN,NaN,1054,NaN,NaN,NaN,NaN,NaN,NaN,NaN 30 | 11/2/2014,225,1731,NaN,4759,20,1,4,1,1,1041,NaN,1070,8,0,1,0,1 31 | 10/31/2014,222,NaN,6525,NaN,NaN,NaN,NaN,NaN,NaN,NaN,2697,NaN,NaN,NaN,NaN,NaN,NaN 32 | 10/29/2014,220,1667,NaN,5338,20,1,4,1,1,1018,NaN,1510,8,0,1,0,1 33 | 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9/9/2014,171,NaN,2407,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN 58 | 9/7/2014,169,861,2081,1424,21,3,NaN,NaN,NaN,557,1137,524,8,0,NaN,NaN,NaN 59 | 9/5/2014,167,812,1871,1261,22,1,NaN,NaN,NaN,517,1089,491,8,NaN,NaN,NaN,NaN 60 | 8/31/2014,162,771,1698,1216,21,1,NaN,NaN,NaN,494,871,476,7,NaN,NaN,NaN,NaN 61 | 8/26/2014,157,648,1378,1026,17,NaN,NaN,NaN,NaN,430,694,422,6,NaN,NaN,NaN,NaN 62 | 8/20/2014,151,607,1082,910,16,NaN,NaN,NaN,NaN,406,624,392,5,NaN,NaN,NaN,NaN 63 | 8/18/2014,149,579,972,907,15,NaN,NaN,NaN,NaN,396,576,374,4,NaN,NaN,NaN,NaN 64 | 8/16/2014,147,543,834,848,15,NaN,NaN,NaN,NaN,394,466,365,4,NaN,NaN,NaN,NaN 65 | 8/13/2014,144,519,786,810,12,NaN,NaN,NaN,NaN,380,413,348,4,NaN,NaN,NaN,NaN 66 | 8/11/2014,142,510,670,783,12,NaN,NaN,NaN,NaN,377,355,334,3,NaN,NaN,NaN,NaN 67 | 8/9/2014,140,506,599,730,13,NaN,NaN,NaN,NaN,373,323,315,2,NaN,NaN,NaN,NaN 68 | 8/6/2014,137,495,554,717,13,NaN,NaN,NaN,NaN,367,294,298,2,NaN,NaN,NaN,NaN 69 | 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6/30/2014,100,413,107,239,NaN,NaN,NaN,NaN,NaN,303,65,99,NaN,NaN,NaN,NaN,NaN 82 | 6/22/2014,92,NaN,51,NaN,NaN,NaN,NaN,NaN,NaN,NaN,34,NaN,NaN,NaN,NaN,NaN,NaN 83 | 6/20/2014,90,390,NaN,158,NaN,NaN,NaN,NaN,NaN,270,NaN,34,NaN,NaN,NaN,NaN,NaN 84 | 6/19/2014,89,NaN,41,NaN,NaN,NaN,NaN,NaN,NaN,NaN,25,NaN,NaN,NaN,NaN,NaN,NaN 85 | 6/18/2014,88,390,NaN,136,NaN,NaN,NaN,NaN,NaN,267,NaN,28,NaN,NaN,NaN,NaN,NaN 86 | 6/17/2014,87,NaN,NaN,97,NaN,NaN,NaN,NaN,NaN,NaN,NaN,49,NaN,NaN,NaN,NaN,NaN 87 | 6/16/2014,86,398,33,NaN,NaN,NaN,NaN,NaN,NaN,264,24,NaN,NaN,NaN,NaN,NaN,NaN 88 | 6/10/2014,80,351,13,89,NaN,NaN,NaN,NaN,NaN,226,24,7,NaN,NaN,NaN,NaN,NaN 89 | 6/5/2014,75,NaN,13,81,NaN,NaN,NaN,NaN,NaN,NaN,NaN,6,NaN,NaN,NaN,NaN,NaN 90 | 6/3/2014,73,344,13,NaN,NaN,NaN,NaN,NaN,NaN,215,12,6,NaN,NaN,NaN,NaN,NaN 91 | 6/1/2014,71,328,13,79,NaN,NaN,NaN,NaN,NaN,208,12,6,NaN,NaN,NaN,NaN,NaN 92 | 5/28/2014,67,291,13,50,NaN,NaN,NaN,NaN,NaN,193,12,6,NaN,NaN,NaN,NaN,NaN 93 | 5/27/2014,66,281,12,16,NaN,NaN,NaN,NaN,NaN,186,11,5,NaN,NaN,NaN,NaN,NaN 94 | 5/23/2014,62,258,12,0,NaN,NaN,NaN,NaN,NaN,174,11,0,NaN,NaN,NaN,NaN,NaN 95 | 5/12/2014,51,248,12,0,NaN,NaN,NaN,NaN,NaN,171,11,0,NaN,NaN,NaN,NaN,NaN 96 | 5/10/2014,49,233,12,0,NaN,NaN,NaN,NaN,NaN,157,11,0,NaN,NaN,NaN,NaN,NaN 97 | 5/7/2014,46,236,13,0,NaN,NaN,NaN,NaN,NaN,158,11,0,NaN,NaN,NaN,NaN,NaN 98 | 5/5/2014,44,235,13,0,NaN,NaN,NaN,NaN,NaN,157,11,0,NaN,NaN,NaN,NaN,NaN 99 | 5/3/2014,42,231,13,0,NaN,NaN,NaN,NaN,NaN,155,11,0,NaN,NaN,NaN,NaN,NaN 100 | 5/1/2014,40,226,13,0,NaN,NaN,NaN,NaN,NaN,149,11,0,NaN,NaN,NaN,NaN,NaN 101 | 4/26/2014,35,224,NaN,0,NaN,NaN,NaN,NaN,NaN,143,NaN,0,NaN,NaN,NaN,NaN,NaN 102 | 4/24/2014,33,NaN,35,0,NaN,NaN,NaN,NaN,NaN,NaN,NaN,0,NaN,NaN,NaN,NaN,NaN 103 | 4/23/2014,32,218,NaN,0,NaN,NaN,NaN,NaN,NaN,141,NaN,0,NaN,NaN,NaN,NaN,NaN 104 | 4/22/2014,31,NaN,NaN,0,NaN,NaN,NaN,NaN,NaN,NaN,NaN,0,NaN,NaN,NaN,NaN,NaN 105 | 4/21/2014,30,NaN,34,NaN,NaN,NaN,NaN,NaN,NaN,NaN,11,NaN,NaN,NaN,NaN,NaN,NaN 106 | 4/20/2014,29,208,NaN,NaN,NaN,NaN,NaN,NaN,NaN,136,6,NaN,NaN,NaN,NaN,NaN,NaN 107 | 4/17/2014,26,203,27,NaN,NaN,NaN,NaN,NaN,NaN,129,NaN,NaN,NaN,NaN,NaN,NaN,NaN 108 | 4/16/2014,25,197,27,NaN,NaN,NaN,NaN,NaN,NaN,122,13,NaN,NaN,NaN,NaN,NaN,NaN 109 | 4/15/2014,24,NaN,NaN,12,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN 110 | 4/14/2014,23,168,NaN,NaN,NaN,NaN,NaN,NaN,NaN,108,NaN,NaN,NaN,NaN,NaN,NaN,NaN 111 | 4/11/2014,20,159,26,2,NaN,NaN,NaN,NaN,NaN,106,13,2,NaN,NaN,NaN,NaN,NaN 112 | 4/9/2014,18,158,25,2,NaN,NaN,NaN,NaN,NaN,101,12,2,NaN,NaN,NaN,NaN,NaN 113 | 4/7/2014,16,151,21,2,NaN,NaN,NaN,NaN,NaN,95,10,2,NaN,NaN,NaN,NaN,NaN 114 | 4/4/2014,13,143,18,2,NaN,NaN,NaN,NaN,NaN,86,7,2,NaN,NaN,NaN,NaN,NaN 115 | 4/1/2014,10,127,8,2,NaN,NaN,NaN,NaN,NaN,83,5,2,NaN,NaN,NaN,NaN,NaN 116 | 3/31/2014,9,122,8,2,NaN,NaN,NaN,NaN,NaN,80,4,2,NaN,NaN,NaN,NaN,NaN 117 | 3/29/2014,7,112,7,NaN,NaN,NaN,NaN,NaN,NaN,70,2,NaN,NaN,NaN,NaN,NaN,NaN 118 | 3/28/2014,6,112,3,2,NaN,NaN,NaN,NaN,NaN,70,3,2,NaN,NaN,NaN,NaN,NaN 119 | 3/27/2014,5,103,8,6,NaN,NaN,NaN,NaN,NaN,66,6,5,NaN,NaN,NaN,NaN,NaN 120 | 3/26/2014,4,86,NaN,NaN,NaN,NaN,NaN,NaN,NaN,62,NaN,NaN,NaN,NaN,NaN,NaN,NaN 121 | 3/25/2014,3,86,NaN,NaN,NaN,NaN,NaN,NaN,NaN,60,NaN,NaN,NaN,NaN,NaN,NaN,NaN 122 | 3/24/2014,2,86,NaN,NaN,NaN,NaN,NaN,NaN,NaN,59,NaN,NaN,NaN,NaN,NaN,NaN,NaN 123 | 3/22/2014,0,49,NaN,NaN,NaN,NaN,NaN,NaN,NaN,29,NaN,NaN,NaN,NaN,NaN,NaN,NaN 124 | -------------------------------------------------------------------------------- /data/intro_to_python_for_finance/exercise_data.csv: -------------------------------------------------------------------------------- 1 | stock_A,stock_B 2 | 45.05767837,19.99379041 3 | 45.68773864,31.13559928 4 | 10.25755511,25.0249542 5 | 27.16968124,22.22046086 6 | 32.79623628,21.21857904 7 | 32.981695,18.96132051 8 | 34.34936356,22.88506299 9 | 33.49890574,27.58631477 10 | 37.42085735,28.79132726 11 | 30.24517285,22.40378714 12 | 24.31967695,26.22762498 13 | 31.9112956,26.11119519 14 | 17.37426295,25.96392218 15 | 33.30352315,23.70975833 16 | 44.94082658,29.67659265 17 | 18.30331939,23.87062331 18 | 36.97197306,28.52377021 19 | 45.32334784,20.32495222 20 | 30.31622165,25.64173702 21 | 17.4110978,24.69840221 22 | 43.68451424,24.83629294 23 | 44.06239326,27.5059677 24 | 22.91458791,26.73721461 25 | 40.18637662,20.39281387 26 | 24.37085851,25.98230499 27 | 30.812186,28.78112108 28 | 43.23460512,20.83866262 29 | 26.16583533,23.3152185 30 | 35.07571872,24.83317862 31 | 27.62183994,27.42677726 32 | 29.26612609,29.36460006 33 | 37.09773447,27.75375618 34 | 43.26169321,29.95885137 35 | 36.64558261,21.61569876 36 | 17.36838931,28.068296 37 | 36.3725971,25.51185536 38 | 33.23830826,23.25957982 39 | 43.50651592,27.80756905 40 | 30.59617118,24.57384812 41 | 45.70718456,25.82704569 42 | 21.38746119,24.06282278 43 | 38.21836318,29.05207346 44 | 30.26262984,23.07783291 45 | 49.96474256,29.20496445 46 | 35.98877065,28.01062187 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21.96691964,15.1656339 82 | 37.69143309,24.05534726 83 | 30.77682946,23.98179296 84 | 50.34901756,23.02678205 85 | 45.51602493,24.73584046 86 | 36.95483617,27.89262999 87 | 19.14839659,32.48281549 88 | 19.12298796,21.34239784 89 | 24.6710159,20.8072402 90 | 33.34094179,25.90455618 91 | 32.58301125,22.56256248 92 | 40.3790292,24.14232869 93 | 40.03470388,20.94211 94 | 21.46874061,23.37715483 95 | 34.22282016,21.63069102 96 | 26.68367604,25.96512559 97 | 29.11812629,23.67025707 98 | 38.71316276,27.7719562 99 | 22.30195366,24.3614312 100 | 27.33509695,30.42724789 101 | 30.30684888,27.15701124 102 | 45.62977526,23.13553483 103 | 11.95933767,22.56447869 104 | 35.06872151,19.41032108 105 | 24.88203789,22.55767462 106 | 36.54545761,24.57617118 107 | 40.26036712,29.66463325 108 | 29.69549906,16.74235463 109 | 26.10037717,24.05875319 110 | 42.63947732,23.87874862 111 | 27.2090664,24.00613483 112 | 22.3568084,20.82072458 113 | 32.56135579,21.26837179 114 | 39.13367378,20.25330619 115 | 22.89435657,25.80524134 116 | 36.72762491,22.76952583 117 | 23.17387118,17.46142999 118 | 32.93119721,18.02773172 119 | 30.04250193,29.66786941 120 | 24.8415361,20.98737868 121 | 41.12628941,28.96424431 122 | 20.34917027,24.83064821 123 | 31.34807273,34.92895242 124 | 34.31519291,24.75547145 125 | 34.27097327,25.34334791 126 | 47.7405671,29.64921421 127 | 22.79543601,25.81550211 128 | 35.05212472,20.06581289 129 | 21.14448652,25.12742012 130 | 34.09867636,27.96329065 131 | 32.01433668,30.05881071 132 | 35.51808578,24.66031138 133 | 24.73542424,28.89180034 134 | 24.16100271,34.63995115 135 | 18.63863186,21.20864319 136 | 49.67577004,25.0995269 137 | 45.28282744,28.61178085 138 | 18.35394926,29.40648478 139 | 32.25669273,23.41897658 140 | 28.43143885,27.27274907 141 | 23.11023908,28.47841909 142 | 28.19394511,26.99283799 143 | 25.2928704,27.27908049 144 | 38.75722155,27.88683224 145 | 41.46153744,24.74455569 146 | 25.34123115,24.59290501 147 | 14.8914978,29.01365224 148 | 39.19538824,19.84660467 149 | 30.40555067,23.9154479 150 | 41.80048632,30.35804668 151 | 34.10023173,28.93834085 152 | 46.04441219,29.33150952 153 | 34.7912171,29.67044828 154 | 25.98331947,30.67961421 155 | 48.2017486,25.25172274 156 | 39.53298921,29.30731673 157 | 30.42154321,24.03936975 158 | 31.95049874,20.3687115 159 | 41.86231367,26.67531556 160 | 26.91325667,28.79449322 161 | 31.57102831,23.06475286 162 | 10.46605702,15.89625482 163 | 28.66807906,21.28444494 164 | 54.96308665,19.74466992 165 | 19.38397983,33.60489409 166 | 40.7366283,19.61593514 167 | 48.43484364,30.25098493 168 | 40.12626522,31.50057378 169 | 15.92875904,28.14725236 170 | 22.55046219,23.90474307 171 | 39.71191922,19.33122609 172 | 20.03917223,21.06480634 173 | 28.41878639,25.94615457 174 | 31.24386423,26.24102692 175 | 27.72342937,24.17785292 176 | 33.38722905,28.68729324 177 | 9.330364634,25.68500623 178 | 30.80988406,26.77500081 179 | 15.24534056,29.11761588 180 | 30.99891071,21.35022339 181 | 27.26533415,29.19339569 182 | 38.85358868,24.06168868 183 | 34.46373506,27.47055012 184 | 28.69664671,25.14578739 185 | 24.68671738,27.231144 186 | 22.67280725,27.28685564 187 | 11.93248687,26.70195748 188 | 25.4437505,27.32920668 189 | 25.22448762,22.34810355 190 | 34.33511809,21.11309386 191 | 25.63755157,20.71686481 192 | 49.58033272,21.82743935 193 | 24.19575597,29.63087321 194 | 55.65059571,24.43416496 195 | 18.09958426,25.51259868 196 | 39.69005255,28.92255858 197 | 37.45700818,23.09909979 198 | 27.90546207,23.82928184 199 | 19.52029094,31.45846568 200 | 28.55205714,24.80594984 201 | 17.76017675,28.61795942 202 | 44.16295627,22.41236307 203 | 30.78900622,22.03031815 204 | 47.32491263,21.52630091 205 | 32.61067836,28.33248301 206 | 20.43413082,12.20373892 207 | 25.43715318,21.21354646 208 | 38.13188126,26.43840203 209 | 19.95906804,21.73414011 210 | 28.06409063,26.19641143 211 | 40.29963763,17.70661152 212 | 28.96229902,27.37727259 213 | 24.89139569,24.6786581 214 | 29.75781313,27.44644642 215 | 46.2363173,16.88802793 216 | 41.86289182,24.8699688 217 | 36.38491697,20.18199289 218 | 30.3064082,21.20178968 219 | 40.56355616,21.32847574 220 | 39.84360862,24.49890024 221 | 37.74531138,22.66443 222 | 37.35134655,20.33037958 223 | 9.536812432,25.7284659 224 | 28.92485643,26.59876916 225 | 18.76162158,29.10890702 226 | 42.93325642,24.39440716 227 | 10.74269148,24.81117729 228 | 24.45674626,25.74159357 229 | 27.92313894,23.44474196 230 | 9.809042092,19.34222887 231 | 31.9987024,24.34882366 232 | 45.63478637,26.98218532 233 | 18.06669556,25.82646886 234 | 27.6904109,33.32433085 235 | 15.68425435,29.82662919 236 | 16.62464036,26.32101697 237 | 54.03103887,23.55851332 238 | 30.03785035,30.52465004 239 | 41.15772249,31.91351841 240 | 40.87901946,20.66661886 241 | 33.77092498,30.15229962 242 | 18.31839316,27.01503519 243 | 38.21339494,29.21728702 244 | 29.53598227,30.54822293 245 | 33.83281376,30.63919479 246 | 33.20421405,30.40492212 247 | 41.69251871,27.06216224 248 | 15.70980963,22.8516818 249 | 50.3413096,23.75533213 250 | 27.93326402,24.05023327 251 | 31.56482887,23.71488745 252 | 25.45179242,26.08725709 253 | 23.12777307,26.93304452 254 | 22.8651791,21.81876245 255 | 25.65215977,24.73347138 256 | 33.76537735,24.52669553 257 | 27.94853384,28.30195534 258 | 26.77645386,20.8755247 259 | 26.2546028,29.81376645 260 | 31.04984908,25.73458252 261 | 24.29736824,30.03600266 262 | 40.83784471,27.99621703 263 | 36.04772383,19.51027726 264 | 41.61469878,23.99096575 265 | 27.81208901,27.97019564 266 | 38.58593994,26.82918066 267 | 18.47842655,27.2317828 268 | 42.4993058,17.53145499 269 | 21.42552738,24.38912811 270 | 18.86399734,22.69318473 271 | 12.17560486,28.50280058 272 | 20.15806136,26.27099285 273 | 42.35472283,21.69303449 274 | 18.17736362,20.43848579 275 | 47.47524008,26.30567698 276 | 42.92530978,29.01458154 277 | 47.87422334,26.53123504 278 | 42.35247549,24.50955146 279 | 30.32570525,27.99088369 280 | 21.31550954,27.02923188 281 | 39.89345635,25.50115341 282 | 33.2057131,25.07772431 283 | 23.35937192,25.2784943 284 | 34.56277403,25.71434754 285 | 39.93534418,15.85096532 286 | 39.81446455,22.90139925 287 | 35.63986118,28.64037785 288 | 24.84220284,21.0734578 289 | 40.59676756,28.17616576 290 | 27.67465384,29.95239855 291 | 30.78073557,29.40575117 292 | 2.705453945,29.45574079 293 | 19.60361791,25.46226723 294 | 15.35257305,23.42808912 295 | 39.88406024,26.40198481 296 | 22.09561672,26.88136329 297 | 40.82533173,28.40210942 298 | 19.69426797,25.05054638 299 | 21.75281554,27.98475134 300 | 12.92487478,25.4006467 301 | 35.30498664,21.7671874 302 | 26.20898578,26.7924324 303 | 30.3744489,24.38028159 304 | 17.91540175,29.00429225 305 | 33.31252906,25.89602926 306 | 28.03020618,25.65421452 307 | 37.20441588,20.77022167 308 | 25.41287797,25.80621811 309 | 27.76995426,22.13911924 310 | 28.18091011,26.49989752 311 | 14.87726829,26.01734067 312 | 35.58828987,22.9234139 313 | 31.43816378,22.88061977 314 | 22.53172326,29.21841508 315 | 43.02568205,31.01075958 316 | 28.77447584,22.50136912 317 | 44.28280919,25.26968252 318 | 37.14272017,20.07718417 319 | 33.45397797,30.08718494 320 | 29.1226011,24.03136508 321 | -0.092846866,28.20751306 322 | 9.573599616,25.30032159 323 | 34.92384775,25.00731938 324 | 21.32454625,26.55095871 325 | 27.14445202,24.36542451 326 | 35.25339715,18.77845165 327 | 22.8293841,22.02121192 328 | 41.81643754,27.76186944 329 | 13.7274386,20.63338338 330 | 21.33232154,23.0117195 331 | 22.39699716,25.8909891 332 | 23.70259103,22.299612 333 | 34.08451116,25.33027364 334 | 30.87641845,24.53616421 335 | 27.86643642,29.19729377 336 | 40.18531096,21.85986092 337 | 23.83087712,25.19493074 338 | 49.560173,23.96221349 339 | 58.29376425,29.05705303 340 | 27.86157401,27.9198508 341 | 28.47758917,17.61204352 342 | 27.14236447,24.28424726 343 | 1.492640791,31.60660502 344 | 29.86086189,20.21637735 345 | 18.98085857,23.85187829 346 | 33.78279779,29.42218182 347 | 30.69146566,27.45281089 348 | 37.02597648,22.55002744 349 | 11.76848487,17.93326078 350 | 32.69281489,27.53633465 351 | 42.7253018,25.00023767 352 | 34.15712362,25.7917272 353 | 29.49166937,24.70935423 354 | 41.00846539,30.17023947 355 | 10.28391913,23.68368922 356 | 31.6987704,21.64390097 357 | 43.18083554,33.22540995 358 | 29.50141807,30.4114813 359 | 26.73117768,31.0667119 360 | 27.77658049,21.36761942 361 | 30.2734872,24.24291244 362 | 21.36111242,26.52616218 363 | 25.17565478,15.25850776 364 | 39.34058938,25.64006123 365 | 47.56893606,27.10975293 366 | 35.54298896,21.38343004 367 | 26.17858423,24.60827751 368 | 26.8306312,24.98836386 369 | 24.69948919,19.64302005 370 | 41.91376458,26.3211048 371 | 35.2186631,28.21744801 372 | 22.63171133,27.97548213 373 | 39.27855305,17.97307914 374 | 53.87017587,35.22032439 375 | 31.82996024,24.11591249 376 | 34.53960049,23.7330745 377 | 22.02637338,23.85195059 378 | 36.67518785,27.55992693 379 | 21.66269229,20.14025436 380 | 40.06551678,24.10103437 381 | 34.27574027,25.87123771 382 | 36.71320929,25.6781541 383 | 26.867017,27.83687247 384 | 51.85700366,27.26685142 385 | 20.04946789,24.42396589 386 | 29.70483595,19.3479376 387 | 28.73887694,18.92956317 388 | 48.96229668,24.48838738 389 | 19.6401895,30.93520996 390 | 32.17857442,20.14822662 391 | 39.66404786,23.36055239 392 | 20.66223528,29.46129871 393 | 21.41818416,17.97414171 394 | 30.41083341,20.39274844 395 | 42.06530931,26.075916 396 | 28.5265286,22.75611502 397 | 40.28829839,23.76932269 398 | 3.313846235,25.39648015 399 | 18.59962215,23.91568915 400 | 42.75103902,22.2946019 401 | 22.54494462,23.50399318 402 | 19.78190636,30.81839017 403 | 36.44985161,14.81175593 404 | 35.18276649,26.66763401 405 | 30.864655,27.26936682 406 | 39.77948877,20.58789453 407 | 36.27776165,21.34224977 408 | 18.72999228,22.06217051 409 | 42.66649385,33.69579976 410 | 12.13950408,22.77188631 411 | 23.45108405,26.73772669 412 | 5.319590119,25.14501573 413 | 33.70468331,25.14830099 414 | 39.01549558,23.33234886 415 | 15.99497681,24.42811853 416 | 23.33282283,23.36067986 417 | 36.82424866,25.09458649 418 | 31.54117023,24.65273367 419 | 50.86836546,23.39620575 420 | 21.05779249,23.97310964 421 | 37.79595395,25.36899077 422 | 30.38186267,24.02093666 423 | 11.70759313,20.7574449 424 | 28.91167459,31.56095052 425 | 24.34676804,25.08454338 426 | 35.21051157,25.12589928 427 | 32.65694048,22.67676743 428 | 35.43708631,25.765317 429 | 23.52604421,30.84350223 430 | 39.71727887,30.39631646 431 | 7.037234574,22.86647716 432 | 26.24378456,25.58767503 433 | 35.54992863,31.03181786 434 | 27.25850277,20.98382643 435 | 39.77140678,25.33336491 436 | 37.62131177,22.36406963 437 | 37.1434374,21.57066013 438 | 44.12155449,24.72193353 439 | 26.58163817,22.33257994 440 | 36.17780147,13.97206076 441 | 21.55874134,29.22105598 442 | 31.62020184,29.20548799 443 | 24.06788589,20.33897125 444 | 37.04545177,23.90747623 445 | 61.44113393,24.04154141 446 | 37.58297619,25.90595229 447 | 43.43704868,29.08188105 448 | 36.64576786,25.68830135 449 | 26.47083146,30.93061694 450 | 31.83797367,25.30005851 451 | 37.079523,26.23924423 452 | 28.70419906,20.17858733 453 | 23.0745678,24.46520385 454 | 44.36473971,27.45363389 455 | 14.5324289,26.77228754 456 | 44.31959089,24.24226373 457 | 45.68891594,21.00072014 458 | 36.63580821,28.18972352 459 | 57.56645116,29.68637922 460 | 13.97114868,26.28442682 461 | 9.867955088,25.32197198 462 | 34.86128572,21.29450332 463 | 16.40592958,25.47831807 464 | 33.50545222,28.00756436 465 | 25.69191119,22.51694238 466 | 35.37843927,27.65533583 467 | 13.13859354,22.59894852 468 | 19.59251048,23.8171239 469 | 28.39073766,24.3148025 470 | 42.19796866,25.21670013 471 | 12.13865304,24.10399693 472 | 32.84049088,22.34705145 473 | 37.7409082,20.08752702 474 | 26.16169395,22.95448815 475 | -1.79360843,26.35787661 476 | 33.8146868,19.9484164 477 | 35.08199419,24.19682951 478 | 25.15456521,19.43755135 479 | 43.93560693,28.76974655 480 | 28.66682805,23.16576036 481 | 21.78490253,29.04123818 482 | 25.08418597,23.53310971 483 | 23.06648212,24.44465498 484 | 30.05337037,32.16340133 485 | 20.07623979,23.49102785 486 | 27.6968277,22.06257192 487 | 22.21952354,29.23789522 488 | 50.41843875,19.79598967 489 | 25.57010785,24.18709867 490 | 27.50216256,27.74859898 491 | 27.72967459,24.59010305 492 | 0.383292698,19.6289374 493 | 32.01487209,32.05943516 494 | 11.85610867,25.7587875 495 | 41.66899788,23.48675358 496 | 20.03121571,23.36432148 497 | 26.54042571,27.45361093 498 | 24.18398383,25.91257901 499 | 30.99769429,22.9695898 500 | 43.82491858,26.10121299 501 | 37.43820925,28.85883248 502 | -------------------------------------------------------------------------------- /data/joining_data_in_sql/countries2/countries.sql: -------------------------------------------------------------------------------- 1 | sCREATE TABLE countries.cities ( 2 | name VARCHAR PRIMARY KEY, 3 | country_code VARCHAR, 4 | city_proper_pop REAL, 5 | metroarea_pop REAL, 6 | urbanarea_pop REAL 7 | ); 8 | 9 | CREATE TABLE countries.countries ( 10 | code VARCHAR PRIMARY KEY, 11 | name VARCHAR, 12 | continent VARCHAR, 13 | region VARCHAR, 14 | surface_area REAL, 15 | indep_year INTEGER, 16 | local_name VARCHAR, 17 | gov_form VARCHAR, 18 | capital VARCHAR, 19 | cap_long REAL, 20 | cap_lat REAL 21 | ); 22 | 23 | CREATE TABLE countries.languages ( 24 | lang_id INTEGER PRIMARY KEY, 25 | code VARCHAR, 26 | name VARCHAR, 27 | percent REAL, 28 | official BOOLEAN 29 | ); 30 | 31 | CREATE TABLE countries.economies ( 32 | econ_id INTEGER PRIMARY KEY, 33 | code VARCHAR, 34 | year INTEGER, 35 | income_group VARCHAR, 36 | gdp_percapita REAL, 37 | gross_savings REAL, 38 | inflation_rate REAL, 39 | total_investment REAL, 40 | unemployment_rate REAL, 41 | exports REAL, 42 | imports REAL 43 | ); 44 | 45 | CREATE TABLE countries.currencies ( 46 | curr_id INTEGER PRIMARY KEY, 47 | code VARCHAR, 48 | basic_unit VARCHAR, 49 | curr_code VARCHAR, 50 | frac_unit VARCHAR, 51 | frac_perbasic REAL 52 | ); 53 | 54 | CREATE TABLE countries.populations ( 55 | pop_id INTEGER PRIMARY KEY, 56 | country_code VARCHAR, 57 | year INTEGER, 58 | fertility_rate REAL, 59 | life_expectancy REAL, 60 | size REAL 61 | ); 62 | 63 | 64 | CREATE TABLE countries.countries_plus ( 65 | name VARCHAR, 66 | continent VARCHAR, 67 | code VARCHAR PRIMARY KEY, 68 | surface_area REAL, 69 | geosize_group VARCHAR 70 | ); 71 | 72 | CREATE TABLE countries.economies2010 ( 73 | code VARCHAR PRIMARY KEY, 74 | year INTEGER, 75 | income_group VARCHAR, 76 | gross_savings REAL 77 | ); 78 | 79 | CREATE TABLE countries.economies2015 ( 80 | code VARCHAR PRIMARY KEY, 81 | year INTEGER, 82 | income_group VARCHAR, 83 | gross_savings REAL 84 | ); 85 | 86 | -- Copy over data from CSVs 87 | copy countries.cities FROM 'D:/PythonProjects/DataCamp/data/joining_data_in_sql/countries2/cities.csv' DELIMITER ',' CSV HEADER; 88 | copy countries.countries FROM 'D:/PythonProjects/DataCamp/data/joining_data_in_sql/countries2/countries.csv' DELIMITER ',' CSV HEADER; 89 | copy countries.languages FROM 'D:/PythonProjects/DataCamp/data/joining_data_in_sql/countries2/languages.csv' DELIMITER ',' CSV HEADER; 90 | copy countries.economies FROM 'D:/PythonProjects/DataCamp/data/joining_data_in_sql/countries2/economies.csv' DELIMITER ',' CSV HEADER; 91 | copy countries.economies2010 FROM 'D:/PythonProjects/DataCamp/data/joining_data_in_sql/countries2/economies2010.csv' DELIMITER ',' CSV HEADER; 92 | copy countries.economies2015 FROM 'D:/PythonProjects/DataCamp/data/joining_data_in_sql/countries2/economies2015.csv' DELIMITER ',' CSV HEADER; 93 | copy countries.currencies FROM 'D:/PythonProjects/DataCamp/data/joining_data_in_sql/countries2/currencies.csv' DELIMITER ',' CSV HEADER; 94 | copy countries.populations FROM 'D:/PythonProjects/DataCamp/data/joining_data_in_sql/countries2/populations.csv' DELIMITER ',' CSV HEADER; 95 | copy countries.countries_plus FROM 'D:/PythonProjects/DataCamp/data/joining_data_in_sql/countries2/countries_plus.csv' DELIMITER ',' CSV HEADER; 96 | 97 | /* 98 | createdb countries 99 | psql countries < data/countries/code/countries.sql 100 | */ -------------------------------------------------------------------------------- /data/joining_data_in_sql/diagrams/diagrams.sql: -------------------------------------------------------------------------------- 1 | CREATE TABLE diagrams.left_table ( 2 | id INTEGER PRIMARY KEY, 3 | val CHARACTER(2) 4 | ); 5 | 6 | CREATE TABLE diagrams.right_table ( 7 | id INTEGER PRIMARY KEY, 8 | val CHARACTER(2) 9 | ); 10 | 11 | CREATE TABLE diagrams.left_one ( 12 | id INTEGER PRIMARY KEY 13 | ); 14 | 15 | CREATE TABLE diagrams.right_one ( 16 | id INTEGER PRIMARY KEY 17 | ); 18 | 19 | CREATE TABLE diagrams.right2 ( 20 | id INTEGER, 21 | val CHARACTER(2) PRIMARY KEY 22 | ); 23 | 24 | CREATE TABLE diagrams.table1 ( 25 | id INTEGER PRIMARY KEY 26 | ); 27 | 28 | CREATE TABLE diagrams.table2 ( 29 | id CHARACTER(1) PRIMARY KEY 30 | ); 31 | 32 | 33 | -- Copy over data from CSVs 34 | copy diagrams.left_table FROM 'D:/PythonProjects/DataCamp/data/joining_data_in_sql/diagrams/left_table.csv' DELIMITER ',' CSV HEADER; 35 | copy diagrams.right_table FROM 'D:/PythonProjects/DataCamp/data/joining_data_in_sql/diagrams/right_table.csv' DELIMITER ',' CSV HEADER; 36 | copy diagrams.left_one FROM 'D:/PythonProjects/DataCamp/data/joining_data_in_sql/diagrams/left_one.csv' DELIMITER ',' CSV HEADER; 37 | copy diagrams.right_one FROM 'D:/PythonProjects/DataCamp/data/joining_data_in_sql/diagrams/right_one.csv' DELIMITER ',' CSV HEADER; 38 | copy diagrams.right2 FROM 'D:/PythonProjects/DataCamp/data/joining_data_in_sql/diagrams/right2.csv' DELIMITER ',' CSV HEADER; 39 | copy diagrams.table1 FROM 'D:/PythonProjects/DataCamp/data/joining_data_in_sql/diagrams/table1.csv' DELIMITER ',' CSV HEADER; 40 | copy diagrams.table2 FROM 'D:/PythonProjects/DataCamp/data/joining_data_in_sql/diagrams/table2.csv' DELIMITER ',' CSV HEADER; 41 | 42 | /* 43 | createdb diagrams 44 | psql diagrams < data/diagrams/diagrams.sql 45 | */ -------------------------------------------------------------------------------- /data/joining_data_in_sql/leaders2/leaders.sql: -------------------------------------------------------------------------------- 1 | CREATE TABLE leaders.presidents ( 2 | country VARCHAR PRIMARY KEY, 3 | continent VARCHAR, 4 | president VARCHAR 5 | ); 6 | 7 | CREATE TABLE leaders.prime_ministers ( 8 | country VARCHAR PRIMARY KEY, 9 | continent VARCHAR, 10 | prime_minister VARCHAR 11 | ); 12 | 13 | CREATE TABLE leaders.states ( 14 | name VARCHAR PRIMARY KEY, 15 | continent VARCHAR, 16 | indep_year INTEGER, 17 | fert_rate REAL, 18 | women_parli_perc REAL 19 | 20 | ); 21 | 22 | CREATE TABLE leaders.monarchs ( 23 | country VARCHAR PRIMARY KEY, 24 | continent VARCHAR, 25 | monarch VARCHAR 26 | ); 27 | 28 | -- Copy over data from CSVs 29 | copy leaders.presidents FROM 'D:/PythonProjects/DataCamp/data/joining_data_in_sql/leaders2/presidents.csv' DELIMITER ',' CSV HEADER; 30 | copy leaders.prime_ministers FROM 'D:/PythonProjects/DataCamp/data/joining_data_in_sql/leaders2/prime_ministers.csv' DELIMITER ',' CSV HEADER; 31 | copy leaders.states FROM 'D:/PythonProjects/DataCamp/data/joining_data_in_sql/leaders2/states.csv' DELIMITER ',' CSV HEADER; 32 | copy leaders.monarchs FROM 'D:/PythonProjects/DataCamp/data/joining_data_in_sql/leaders2/monarchs.csv' DELIMITER ',' CSV HEADER; 33 | 34 | /* 35 | createdb leaders 36 | psql leaders < data/leaders/leaders.sql 37 | */ -------------------------------------------------------------------------------- /data/manipulating-dataframes-with-pandas/LURReport.csv: -------------------------------------------------------------------------------- 1 | Area,Month,Year,Labor Force,Employed,Unemployed,Area Rate ,Massachusetts Rate 2 | Barnstable County,December,2018,"112,449","107,669","4,780",4.3,2.7 3 | Barnstable County,November,2018,"112,150","108,171","3,979",3.5,2.6 4 | Barnstable County,October,2018,"115,665","112,127","3,538",3.1,2.9 5 | Barnstable County,September,2018,"119,420","115,757","3,663",3.1,3.2 6 | Barnstable County,August,2018,"129,627","125,636","3,991",3.1,3.5 7 | Barnstable County,July,2018,"131,087","126,628","4,459",3.4,3.9 8 | Barnstable County,June,2018,"124,401","119,744","4,657",3.7,3.9 9 | Barnstable County,May,2018,"116,265","111,701","4,564",3.9,3.3 10 | Barnstable County,April,2018,"110,839","105,688","5,151",4.6,3.3 11 | Barnstable County,March,2018,"108,760","101,781","6,979",6.4,3.8 12 | Barnstable County,February,2018,"107,573","99,898","7,675",7.1,4 13 | Barnstable County,January,2018,"107,698","100,330","7,368",6.8,4 14 | Barnstable County,December,2017,"107,563","102,507","5,056",4.7,3.1 15 | Berkshire County,December,2018,"66,571","64,139","2,432",3.7,2.7 16 | Berkshire County,November,2018,"65,712","63,616","2,096",3.2,2.6 17 | Berkshire County,October,2018,"66,167","64,031","2,136",3.2,2.9 18 | Berkshire County,September,2018,"66,366","64,053","2,313",3.5,3.2 19 | Berkshire County,August,2018,"68,406","65,884","2,522",3.7,3.5 20 | Berkshire County,July,2018,"69,949","67,092","2,857",4.1,3.9 21 | Berkshire County,June,2018,"68,400","65,463","2,937",4.3,3.9 22 | Berkshire County,May,2018,"65,745","63,072","2,673",4.1,3.3 23 | Berkshire County,April,2018,"64,426","61,631","2,795",4.3,3.3 24 | Berkshire County,March,2018,"64,510","61,215","3,295",5.1,3.8 25 | Berkshire County,February,2018,"64,485","61,072","3,413",5.3,4 26 | Berkshire County,January,2018,"64,373","61,070","3,303",5.1,4 27 | Berkshire County,December,2017,"64,579","62,125","2,454",3.8,3.1 28 | Bristol County,December,2018,"304,341","294,085","10,256",3.4,2.7 29 | Bristol County,November,2018,"303,750","294,269","9,481",3.1,2.6 30 | Bristol County,October,2018,"304,076","293,509","10,567",3.5,2.9 31 | Bristol County,September,2018,"303,265","291,641","11,624",3.8,3.2 32 | Bristol County,August,2018,"305,928","292,397","13,531",4.4,3.5 33 | Bristol County,July,2018,"308,011","293,140","14,871",4.8,3.9 34 | Bristol County,June,2018,"306,183","291,876","14,307",4.7,3.9 35 | Bristol County,May,2018,"302,013","289,506","12,507",4.1,3.3 36 | Bristol County,April,2018,"299,694","287,007","12,687",4.2,3.3 37 | Bristol County,March,2018,"299,922","284,331","15,591",5.2,3.8 38 | Bristol County,February,2018,"297,971","281,284","16,687",5.6,4 39 | Bristol County,January,2018,"294,378","277,743","16,635",5.7,4 40 | Bristol County,December,2017,"290,220","278,825","11,395",3.9,3.1 41 | Dukes County,December,2018,"8,865","8,405",460,5.2,2.7 42 | Dukes County,November,2018,"8,750","8,373",377,4.3,2.6 43 | Dukes County,October,2018,"9,541","9,257",284,3,2.9 44 | Dukes County,September,2018,"10,737","10,462",275,2.6,3.2 45 | Dukes County,August,2018,"12,585","12,294",291,2.3,3.5 46 | Dukes County,July,2018,"12,714","12,370",344,2.7,3.9 47 | Dukes County,June,2018,"11,375","10,996",379,3.3,3.9 48 | Dukes County,May,2018,"9,532","9,106",426,4.5,3.3 49 | Dukes County,April,2018,"8,702","8,210",492,5.7,3.3 50 | Dukes County,March,2018,"8,226","7,549",677,8.2,3.8 51 | Dukes County,February,2018,"8,201","7,458",743,9.1,4 52 | Dukes County,January,2018,"8,250","7,528",722,8.8,4 53 | Dukes County,December,2017,"8,464","7,962",502,5.9,3.1 54 | Essex County,December,2018,"433,008","420,989","12,019",2.8,2.7 55 | Essex County,November,2018,"431,741","420,296","11,445",2.7,2.6 56 | Essex County,October,2018,"432,315","419,467","12,848",3,2.9 57 | Essex County,September,2018,"431,557","417,359","14,198",3.3,3.2 58 | Essex County,August,2018,"436,368","420,552","15,816",3.6,3.5 59 | Essex County,July,2018,"439,379","421,934","17,445",4,3.9 60 | Essex County,June,2018,"436,870","419,515","17,355",4,3.9 61 | Essex County,May,2018,"430,595","415,733","14,862",3.5,3.3 62 | Essex County,April,2018,"426,036","411,624","14,412",3.4,3.3 63 | Essex County,March,2018,"423,434","407,011","16,423",3.9,3.8 64 | Essex County,February,2018,"420,022","402,969","17,053",4.1,4 65 | Essex County,January,2018,"414,598","398,011","16,587",4,4 66 | Essex County,December,2017,"412,665","399,564","13,101",3.2,3.1 67 | Franklin County,December,2018,"41,607","40,524","1,083",2.6,2.7 68 | Franklin County,November,2018,"41,248","40,280",968,2.3,2.6 69 | Franklin County,October,2018,"41,259","40,212","1,047",2.5,2.9 70 | Franklin County,September,2018,"41,102","39,967","1,135",2.8,3.2 71 | Franklin County,August,2018,"41,188","39,903","1,285",3.1,3.5 72 | Franklin County,July,2018,"41,441","40,049","1,392",3.4,3.9 73 | Franklin County,June,2018,"41,490","40,041","1,449",3.5,3.9 74 | Franklin County,May,2018,"41,039","39,824","1,215",3,3.3 75 | Franklin County,April,2018,"40,871","39,600","1,271",3.1,3.3 76 | Franklin County,March,2018,"40,655","39,192","1,463",3.6,3.8 77 | Franklin County,February,2018,"40,362","38,810","1,552",3.8,4 78 | Franklin County,January,2018,"39,730","38,225","1,505",3.8,4 79 | Franklin County,December,2017,"39,833","38,678","1,155",2.9,3.1 80 | Hampden County,December,2018,"232,951","224,113","8,838",3.8,2.7 81 | Hampden County,November,2018,"232,117","223,822","8,295",3.6,2.6 82 | Hampden County,October,2018,"231,798","222,577","9,221",4,2.9 83 | Hampden County,September,2018,"230,729","220,584","10,145",4.4,3.2 84 | Hampden County,August,2018,"231,593","219,915","11,678",5,3.5 85 | Hampden County,July,2018,"232,554","219,519","13,035",5.6,3.9 86 | Hampden County,June,2018,"232,943","220,387","12,556",5.4,3.9 87 | Hampden County,May,2018,"230,719","220,003","10,716",4.6,3.3 88 | Hampden County,April,2018,"229,682","219,444","10,238",4.5,3.3 89 | Hampden County,March,2018,"229,339","217,572","11,767",5.1,3.8 90 | Hampden County,February,2018,"227,580","215,448","12,132",5.3,4 91 | Hampden County,January,2018,"222,667","210,723","11,944",5.4,4 92 | Hampden County,December,2017,"222,532","213,357","9,175",4.1,3.1 93 | Hampshire County,December,2018,"92,572","90,380","2,192",2.4,2.7 94 | Hampshire County,November,2018,"92,310","90,257","2,053",2.2,2.6 95 | Hampshire County,October,2018,"92,103","89,766","2,337",2.5,2.9 96 | Hampshire County,September,2018,"91,575","88,959","2,616",2.9,3.2 97 | Hampshire County,August,2018,"91,726","88,704","3,022",3.3,3.5 98 | Hampshire County,July,2018,"92,173","88,558","3,615",3.9,3.9 99 | Hampshire County,June,2018,"92,514","88,898","3,616",3.9,3.9 100 | Hampshire County,May,2018,"91,428","88,728","2,700",3,3.3 101 | Hampshire County,April,2018,"91,148","88,498","2,650",2.9,3.3 102 | Hampshire County,March,2018,"90,848","87,738","3,110",3.4,3.8 103 | Hampshire County,February,2018,"89,998","86,878","3,120",3.5,4 104 | Hampshire County,January,2018,"87,957","84,972","2,985",3.4,4 105 | Hampshire County,December,2017,"88,308","86,052","2,256",2.6,3.1 106 | Middlesex County,December,2018,"928,123","908,372","19,751",2.1,2.7 107 | Middlesex County,November,2018,"926,517","907,102","19,415",2.1,2.6 108 | Middlesex County,October,2018,"927,895","905,632","22,263",2.4,2.9 109 | Middlesex County,September,2018,"923,987","899,506","24,481",2.6,3.2 110 | Middlesex County,August,2018,"935,798","907,953","27,845",3,3.5 111 | Middlesex County,July,2018,"941,731","910,422","31,309",3.3,3.9 112 | Middlesex County,June,2018,"933,026","902,342","30,684",3.3,3.9 113 | Middlesex County,May,2018,"917,849","892,398","25,451",2.8,3.3 114 | Middlesex County,April,2018,"910,542","886,478","24,064",2.6,3.3 115 | Middlesex County,March,2018,"902,794","875,862","26,932",3,3.8 116 | Middlesex County,February,2018,"896,301","868,308","27,993",3.1,4 117 | Middlesex County,January,2018,"885,568","858,394","27,174",3.1,4 118 | Middlesex County,December,2017,"881,917","859,999","21,918",2.5,3.1 119 | Nantucket County,December,2018,"6,675","6,269",406,6.1,2.7 120 | Nantucket County,November,2018,"6,582","6,339",243,3.7,2.6 121 | Nantucket County,October,2018,"7,517","7,345",172,2.3,2.9 122 | Nantucket County,September,2018,"8,480","8,321",159,1.9,3.2 123 | Nantucket County,August,2018,"9,889","9,719",170,1.7,3.5 124 | Nantucket County,July,2018,"10,011","9,807",204,2,3.9 125 | Nantucket County,June,2018,"9,021","8,797",224,2.5,3.9 126 | Nantucket County,May,2018,"7,426","7,178",248,3.3,3.3 127 | Nantucket County,April,2018,"6,354","6,024",330,5.2,3.3 128 | Nantucket County,March,2018,"5,873","5,312",561,9.6,3.8 129 | Nantucket County,February,2018,"5,804","5,149",655,11.3,4 130 | Nantucket County,January,2018,"5,828","5,193",635,10.9,4 131 | Nantucket County,December,2017,"6,298","5,936",362,5.7,3.1 132 | Norfolk County,December,2018,"397,178","387,837","9,341",2.4,2.7 133 | Norfolk County,November,2018,"396,632","387,297","9,335",2.4,2.6 134 | Norfolk County,October,2018,"397,030","386,369","10,661",2.7,2.9 135 | Norfolk County,September,2018,"395,275","383,586","11,689",3,3.2 136 | Norfolk County,August,2018,"400,103","387,166","12,937",3.2,3.5 137 | Norfolk County,July,2018,"402,589","388,062","14,527",3.6,3.9 138 | Norfolk County,June,2018,"398,625","384,495","14,130",3.5,3.9 139 | Norfolk County,May,2018,"392,297","380,481","11,816",3,3.3 140 | Norfolk County,April,2018,"389,011","377,802","11,209",2.9,3.3 141 | Norfolk County,March,2018,"385,624","373,179","12,445",3.2,3.8 142 | Norfolk County,February,2018,"382,982","369,946","13,036",3.4,4 143 | Norfolk County,January,2018,"377,423","364,959","12,464",3.3,4 144 | Norfolk County,December,2017,"376,343","366,164","10,179",2.7,3.1 145 | Plymouth County,December,2018,"286,934","278,875","8,059",2.8,2.7 146 | Plymouth County,November,2018,"286,318","278,775","7,543",2.6,2.6 147 | Plymouth County,October,2018,"286,796","278,304","8,492",3,2.9 148 | Plymouth County,September,2018,"286,265","276,902","9,363",3.3,3.2 149 | Plymouth County,August,2018,"290,660","279,892","10,768",3.7,3.5 150 | Plymouth County,July,2018,"292,295","280,267","12,028",4.1,3.9 151 | Plymouth County,June,2018,"288,932","277,500","11,432",4,3.9 152 | Plymouth County,May,2018,"284,378","274,538","9,840",3.5,3.3 153 | Plymouth County,April,2018,"281,885","272,171","9,714",3.4,3.3 154 | Plymouth County,March,2018,"280,092","268,531","11,561",4.1,3.8 155 | Plymouth County,February,2018,"278,169","266,023","12,146",4.4,4 156 | Plymouth County,January,2018,"274,348","262,564","11,784",4.3,4 157 | Plymouth County,December,2017,"272,532","263,879","8,653",3.2,3.1 158 | Suffolk County,December,2018,"454,969","444,261","10,708",2.4,2.7 159 | Suffolk County,November,2018,"454,483","443,624","10,859",2.4,2.6 160 | Suffolk County,October,2018,"454,877","442,502","12,375",2.7,2.9 161 | Suffolk County,September,2018,"452,833","439,298","13,535",3,3.2 162 | Suffolk County,August,2018,"459,050","443,550","15,500",3.4,3.5 163 | Suffolk County,July,2018,"462,221","444,584","17,637",3.8,3.9 164 | Suffolk County,June,2018,"457,978","440,343","17,635",3.9,3.9 165 | Suffolk County,May,2018,"449,595","435,646","13,949",3.1,3.3 166 | Suffolk County,April,2018,"445,224","432,656","12,568",2.8,3.3 167 | Suffolk County,March,2018,"441,494","427,306","14,188",3.2,3.8 168 | Suffolk County,February,2018,"438,304","423,656","14,648",3.3,4 169 | Suffolk County,January,2018,"432,516","417,903","14,613",3.4,4 170 | Suffolk County,December,2017,"431,120","419,250","11,870",2.8,3.1 171 | Worcester County,December,2018,"455,662","442,433","13,229",2.9,2.7 172 | Worcester County,November,2018,"454,180","441,899","12,281",2.7,2.6 173 | Worcester County,October,2018,"453,461","439,684","13,777",3,2.9 174 | Worcester County,September,2018,"453,155","437,983","15,172",3.3,3.2 175 | Worcester County,August,2018,"453,830","436,369","17,461",3.8,3.5 176 | Worcester County,July,2018,"456,007","436,660","19,347",4.2,3.9 177 | Worcester County,June,2018,"455,645","436,877","18,768",4.1,3.9 178 | Worcester County,May,2018,"450,629","434,716","15,913",3.5,3.3 179 | Worcester County,April,2018,"446,967","431,353","15,614",3.5,3.3 180 | Worcester County,March,2018,"445,187","427,351","17,836",4,3.8 181 | Worcester County,February,2018,"442,732","424,152","18,580",4.2,4 182 | Worcester County,January,2018,"436,938","418,883","18,055",4.1,4 183 | Worcester County,December,2017,"434,143","420,376","13,767",3.2,3.1 184 | -------------------------------------------------------------------------------- /data/manipulating-dataframes-with-pandas/sales2.csv: -------------------------------------------------------------------------------- 1 | state,month,eggs,salt,spam 2 | CA,1,47,12,17 3 | CA,2,110,50,31 4 | NY,1,221,89,72 5 | NY,2,77,87,20 6 | TX,1,132,,52 7 | TX,2,205,60,55 8 | -------------------------------------------------------------------------------- /data/merging-dataframes-with-pandas.7z: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/trenton3983/DataCamp/69fb59a5340f4d0340de24e1db305d859b6ae602/data/merging-dataframes-with-pandas.7z -------------------------------------------------------------------------------- /data/sales_data/sales-feb-2015.csv: -------------------------------------------------------------------------------- 1 | Date,Company,Product,Units 2 | 2015/2/2 8:33:00 AM,Hooli,Software,3 3 | 2015/2/2 8:54:00 PM,Mediacore,Hardware,9 4 | 2015/2/3 2:14:00 PM,Initech,Software,13 5 | 2015/2/4 3:36:00 PM,Streeplex,Software,13 6 | 2015/2/4 9:52:00 PM,Acme Coporation,Hardware,14 7 | 2015/2/5 1:53:00 AM,Acme Coporation,Software,19 8 | 2015/2/5 10:05:00 PM,Hooli,Service,10 9 | 2015/2/7 10:58:00 PM,Acme Coporation,Hardware,1 10 | 2015/2/9 8:57:00 AM,Streeplex,Service,19 11 | 2015/2/9 1:09:00 PM,Mediacore,Software,7 12 | 2015/2/11 8:03:00 PM,Initech,Software,7 13 | 2015/2/11 10:50:00 PM,Hooli,Software,4 14 | 2015/2/16 12:09:00 PM,Hooli,Software,10 15 | 2015/2/19 10:59:00 AM,Mediacore,Hardware,16 16 | 2015/2/19 4:02:00 PM,Mediacore,Service,10 17 | 2015/2/21 5:01:00 AM,Mediacore,Software,3 18 | 2015/2/21 8:41:00 PM,Hooli,Hardware,3 19 | 2015/2/25 12:29:00 AM,Initech,Service,10 20 | 2015/2/26 8:57:00 AM,Streeplex,Service,4 21 | 2015/2/26 8:58:00 AM,Streeplex,Service,1 22 | -------------------------------------------------------------------------------- /data/stocks.7z: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/trenton3983/DataCamp/69fb59a5340f4d0340de24e1db305d859b6ae602/data/stocks.7z -------------------------------------------------------------------------------- /data/survey.csv: -------------------------------------------------------------------------------- 1 | taken,person,quant,reading 2 | 619,dyer,rad,9.82 3 | 619,dyer,sal,0.13 4 | 622,dyer,rad,7.8 5 | 622,dyer,sal,0.09 6 | 734,pb,rad,8.41 7 | 734,lake,sal,0.05 8 | 734,pb,temp,-21.5 9 | 735,pb,rad,7.22 10 | 735,NaN,sal,0.06 11 | 735,NaN,temp,-26 12 | 751,pb,rad,4.35 13 | 751,pb,temp,-18.5 14 | 751,lake,sal,0.1 15 | 752,lake,rad,2.19 16 | 752,lake,sal,0.09 17 | 752,lake,temp,-16 18 | 752,roe,sal,41.6 19 | 837,lake,rad,1.46 20 | 837,lake,sal,0.21 21 | 837,roe,sal,22.5 22 | 844,roe,rad,11.25 23 | -------------------------------------------------------------------------------- /data/tb.csv: -------------------------------------------------------------------------------- 1 | country,year,m014,m1524,m2534,m3544,m4554,m5564,m65,mu,f014,f1524,f2534,f3544,f4554,f5564,f65,fu 2 | AD,2000,0,0,1,0,0,0,0,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN 3 | AE,2000,2,4,4,6,5,12,10,NaN,3,16,1,3,0,0,4,NaN 4 | AF,2000,52,228,183,149,129,94,80,NaN,93,414,565,339,205,99,36,NaN 5 | AG,2000,0,0,0,0,0,0,1,NaN,1,1,1,0,0,0,0,NaN 6 | AL,2000,2,19,21,14,24,19,16,NaN,3,11,10,8,8,5,11,NaN 7 | AM,2000,2,152,130,131,63,26,21,NaN,1,24,27,24,8,8,4,NaN 8 | AN,2000,0,0,1,2,0,0,0,NaN,0,0,1,0,0,1,0,NaN 9 | AO,2000,186,999,1003,912,482,312,194,NaN,247,1142,1091,844,417,200,120,NaN 10 | AR,2000,97,278,594,402,419,368,330,NaN,121,544,479,262,230,179,216,NaN 11 | AS,2000,NaN,NaN,NaN,NaN,1,1,NaN,NaN,NaN,NaN,NaN,NaN,1,NaN,NaN,NaN 12 | AT,2000,1,17,30,59,42,23,41,NaN,1,11,22,12,11,6,22,NaN 13 | AU,2000,3,16,35,25,24,19,49,NaN,0,15,19,12,15,5,14,NaN 14 | AZ,2000,0,9,24,33,42,30,0,NaN,0,3,3,6,3,0,0,NaN 15 | BA,2000,4,56,82,99,66,58,77,NaN,4,30,46,29,29,48,124,NaN 16 | BB,2000,0,0,0,2,0,0,0,NaN,0,0,1,0,0,0,0,NaN 17 | BD,2000,256,3640,5643,5750,4718,3667,2837,NaN,495,3029,3238,2247,1315,778,370,NaN 18 | BE,2000,3,20,57,39,55,32,56,NaN,6,15,15,19,4,13,27,NaN 19 | BF,2000,12,91,274,252,133,68,65,NaN,7,59,128,101,45,38,14,NaN 20 | BG,2000,0,13,16,20,3,9,10,NaN,0,11,14,7,3,4,6,NaN 21 | BH,2000,0,0,3,2,5,3,4,NaN,0,1,2,0,1,1,1,NaN 22 | BI,2000,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN 23 | BJ,2000,19,277,428,327,213,103,74,NaN,36,239,275,149,76,45,25,NaN 24 | BM,2000,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN 25 | BN,2000,0,6,4,15,5,7,15,NaN,0,4,6,9,6,3,4,NaN 26 | BO,2000,166,1182,797,518,466,340,366,NaN,191,831,588,334,254,192,233,NaN 27 | BR,2000,1894,7268,11568,11906,8623,5085,4494,NaN,1859,6719,7215,5395,3582,2384,2496,NaN 28 | BS,2000,1,2,7,9,4,3,2,NaN,2,5,7,8,2,3,1,NaN 29 | BT,2000,6,65,41,30,24,12,2,NaN,7,57,34,31,23,3,2,NaN 30 | BW,2000,25,185,605,488,267,135,96,NaN,37,335,469,262,98,57,36,NaN 31 | BY,2000,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN 32 | BZ,2000,2,5,7,2,6,3,5,NaN,0,2,1,2,4,1,4,NaN 33 | CA,2000,5,34,45,46,41,32,79,NaN,4,33,40,30,25,12,66,NaN 34 | CD,2000,485,4048,5833,4151,2549,1295,602,NaN,718,4422,5146,3309,1724,855,351,NaN 35 | CF,2000,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN 36 | CG,2000,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN 37 | CH,2000,0,5,18,10,7,5,8,NaN,1,9,12,8,2,1,6,NaN 38 | CI,2000,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN 39 | CK,2000,0,0,0,0,0,0,0,NaN,0,0,0,0,0,0,0,NaN 40 | CL,2000,6,81,160,198,150,132,126,NaN,10,66,96,70,54,58,83,NaN 41 | CM,2000,41,518,842,584,284,130,75,NaN,63,368,530,293,139,60,33,NaN 42 | CN,2000,1131,19111,29399,25206,25593,21429,21771,NaN,1420,14536,18496,12377,9899,7102,6296,NaN 43 | CO,2000,246,763,1030,963,743,610,746,NaN,194,587,758,523,381,304,510,NaN 44 | CR,2000,14,31,53,62,39,28,49,NaN,13,21,33,24,20,23,24,NaN 45 | CU,2000,0,71,167,90,74,55,75,NaN,2,9,22,26,22,23,39,NaN 46 | CV,2000,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN 47 | CY,2000,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN 48 | CZ,2000,0,7,31,52,89,61,59,NaN,0,15,13,9,10,7,57,NaN 49 | DE,2000,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN 50 | DJ,2000,17,302,347,139,67,60,42,NaN,12,147,156,47,31,17,10,NaN 51 | DK,2000,5,10,20,24,16,11,14,NaN,5,16,15,14,6,7,8,NaN 52 | DO,2000,73,410,481,344,173,125,113,NaN,65,317,325,212,115,79,75,NaN 53 | DZ,2000,59,927,1516,610,491,234,299,NaN,36,1005,1293,746,314,208,312,NaN 54 | EC,2000,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN 55 | EE,2000,0,6,31,53,56,35,15,NaN,0,9,11,14,11,4,10,NaN 56 | EG,2000,21,641,827,667,476,307,158,NaN,55,457,343,257,211,112,48,NaN 57 | ER,2000,9,70,75,57,32,25,20,NaN,10,100,87,71,21,12,8,NaN 58 | ES,2000,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN 59 | ET,2000,915,5095,5187,3082,1495,610,397,NaN,1037,4699,4424,2105,976,366,122,NaN 60 | FI,2000,0,3,8,22,19,28,53,NaN,0,1,5,3,4,6,49,NaN 61 | FJ,2000,0,8,6,13,5,4,2,NaN,0,7,5,7,1,4,0,NaN 62 | FM,2000,0,2,0,1,0,0,1,NaN,4,3,1,1,0,1,1,NaN 63 | FR,2000,10,136,248,247,211,125,244,NaN,18,108,127,89,46,43,155,NaN 64 | GB,2000,8,86,130,96,87,75,138,NaN,9,95,114,60,31,31,67,NaN 65 | GD,2000,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN 66 | GE,2000,4,76,111,113,63,45,28,NaN,1,49,37,33,17,10,5,NaN 67 | GH,2000,73,550,1266,1115,811,495,426,NaN,74,456,791,566,338,179,176,NaN 68 | GN,2000,39,551,860,570,282,203,103,NaN,66,314,446,245,114,82,45,NaN 69 | GR,2000,1,10,22,32,24,19,46,NaN,0,2,9,10,5,6,25,NaN 70 | GT,2000,36,220,236,216,177,112,140,NaN,41,199,167,175,135,87,111,NaN 71 | GU,2000,2,1,6,6,9,6,9,NaN,0,3,1,2,5,2,2,NaN 72 | GW,2000,2,52,92,80,64,39,19,NaN,4,30,46,47,24,15,12,NaN 73 | GY,2000,4,20,19,14,7,6,9,NaN,1,11,8,7,5,5,3,NaN 74 | HK,2000,4,78,102,160,211,236,578,NaN,5,65,115,86,44,45,211,NaN 75 | HN,2000,30,123,371,246,277,214,43,NaN,25,21,269,258,270,160,38,NaN 76 | HR,2000,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN 77 | HT,2000,67,836,898,613,350,147,118,NaN,96,914,857,513,275,132,71,NaN 78 | HU,2000,0,8,24,85,104,58,27,NaN,1,7,17,19,22,10,30,NaN 79 | ID,2000,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN 80 | IE,2000,0,10,7,7,6,4,12,NaN,0,13,8,13,6,7,15,NaN 81 | IL,2000,0,16,28,17,24,10,31,NaN,2,11,15,7,3,7,25,NaN 82 | IN,2000,1588,20963,31090,30829,24230,15308,8534,NaN,2250,14495,17287,11768,7516,4594,2697,NaN 83 | IQ,2000,21,627,317,297,205,135,101,NaN,37,338,241,136,134,103,87,NaN 84 | IR,2000,29,438,467,387,295,344,642,NaN,77,593,410,322,320,407,647,NaN 85 | IS,2000,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,0,NaN,1,NaN,NaN,NaN,NaN,NaN 86 | IT,2000,12,63,96,75,58,54,112,NaN,6,38,58,33,13,19,39,NaN 87 | JM,2000,0,6,13,13,15,6,5,NaN,1,8,8,7,2,5,1,NaN 88 | JO,2000,0,8,16,13,9,14,2,NaN,0,8,9,1,2,2,5,NaN 89 | JP,2000,2,246,572,676,1494,1509,3816,NaN,5,222,464,213,292,384,1958,NaN 90 | KE,2000,264,3739,6653,3548,1630,630,414,NaN,416,3916,4363,1874,831,347,148,NaN 91 | KG,2000,4,128,227,205,115,52,46,NaN,6,128,146,100,41,30,29,NaN 92 | KH,2000,26,519,1323,1618,1456,1373,1058,NaN,38,457,1157,1649,1798,1459,892,NaN 93 | KI,2000,2,9,3,3,3,8,2,NaN,2,5,6,3,4,1,3,NaN 94 | KM,2000,0,18,7,14,9,3,4,NaN,1,9,6,12,1,2,1,NaN 95 | KN,2000,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN 96 | KP,2000,293,928,1508,2927,2519,1167,651,NaN,167,683,1121,2004,1524,591,357,NaN 97 | KR,2000,19,821,1085,988,853,731,901,NaN,25,546,544,393,220,295,795,NaN 98 | KW,2000,0,10,44,32,21,11,5,NaN,1,11,24,12,5,3,1,NaN 99 | KY,2000,0,0,3,1,0,1,0,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN 100 | KZ,2000,36,1057,1409,1379,923,439,218,NaN,84,999,1079,599,275,202,204,NaN 101 | LA,2000,7,92,128,166,201,177,176,NaN,10,59,95,131,122,91,71,NaN 102 | LB,2000,5,16,28,20,15,17,14,NaN,4,31,26,9,7,4,6,NaN 103 | LC,2000,0,0,0,1,0,1,2,NaN,0,1,0,1,0,1,0,NaN 104 | LK,2000,25,266,459,695,793,484,360,NaN,23,312,264,176,202,144,113,NaN 105 | LR,2000,12,133,196,127,52,17,26,NaN,21,140,149,88,28,16,16,NaN 106 | LS,2000,8,165,458,517,395,198,76,NaN,11,222,336,195,83,36,29,NaN 107 | LT,2000,1,38,97,145,155,74,68,NaN,0,20,37,39,32,22,48,NaN 108 | LU,2000,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN 109 | LV,2000,0,53,106,124,111,64,34,NaN,2,25,41,27,28,7,15,NaN 110 | LY,2000,5,101,239,86,36,29,32,NaN,6,43,35,24,24,16,22,NaN 111 | MA,2000,99,2061,2423,1705,855,485,595,NaN,170,1530,1121,672,398,406,352,NaN 112 | MC,2000,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN 113 | MD,2000,2,52,31,36,13,13,6,NaN,1,16,32,45,23,14,6,NaN 114 | MG,2000,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN 115 | MH,2000,3,5,4,1,3,5,3,NaN,7,7,3,0,2,2,0,NaN 116 | MK,2000,5,8,14,20,19,20,14,NaN,1,15,14,17,5,5,10,NaN 117 | ML,2000,23,206,430,396,297,235,144,NaN,14,174,232,152,106,75,43,NaN 118 | MM,2000,88,1459,2636,2781,2161,1235,836,NaN,72,1040,1592,1397,987,592,378,NaN 119 | MN,2000,6,181,260,171,68,38,23,NaN,32,200,213,113,41,26,17,NaN 120 | MO,2000,0,10,8,25,22,9,17,NaN,0,10,4,6,6,3,13,NaN 121 | MP,2000,1,4,8,9,9,3,2,NaN,0,10,17,7,3,1,1,NaN 122 | MR,2000,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN 123 | MS,2000,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN 124 | MT,2000,0,1,0,1,1,0,1,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN 125 | MU,2000,2,6,9,18,19,14,8,NaN,1,5,8,8,6,7,4,NaN 126 | MV,2000,0,9,10,2,5,5,3,NaN,0,11,4,5,4,5,2,NaN 127 | MW,2000,50,653,1476,1113,585,245,114,NaN,66,1038,1481,831,401,148,64,NaN 128 | MX,2000,214,1079,1387,1162,1235,972,1126,NaN,176,663,828,698,832,595,709,NaN 129 | MY,2000,32,694,1138,1177,908,814,891,NaN,41,464,564,424,367,356,286,NaN 130 | MZ,2000,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN 131 | NaN,2000,18,269,874,665,300,147,81,NaN,16,352,654,348,161,76,52,NaN 132 | NC,2000,1,1,3,4,2,3,4,NaN,1,8,1,1,3,2,4,NaN 133 | NE,2000,29,270,174,441,252,151,78,NaN,31,123,206,168,151,63,9,NaN 134 | NG,2000,157,2173,3164,1836,1091,566,463,NaN,239,2934,2434,1110,676,344,231,NaN 135 | NI,2000,18,194,174,147,108,64,90,NaN,34,188,173,98,76,46,61,NaN 136 | NL,2000,0,34,63,41,25,10,21,NaN,4,29,22,16,9,5,10,NaN 137 | NO,2000,0,1,9,3,6,2,4,NaN,1,3,1,NaN,NaN,2,5,NaN 138 | NP,2000,170,1904,1763,1713,1491,1294,772,NaN,176,1267,1078,833,575,419,228,NaN 139 | NR,2000,NaN,NaN,NaN,NaN,1,NaN,NaN,NaN,NaN,NaN,NaN,NaN,1,1,NaN,NaN 140 | NU,2000,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN 141 | NZ,2000,0,6,5,6,8,10,7,NaN,1,6,6,5,0,4,10,NaN 142 | OM,2000,1,8,9,11,12,9,11,NaN,2,17,5,7,5,11,6,NaN 143 | PA,2000,3,44,78,61,37,27,26,NaN,6,43,34,35,19,12,16,NaN 144 | PE,2000,552,5290,2875,1546,1041,801,796,NaN,633,3686,2472,1156,609,499,624,NaN 145 | PF,2000,1,3,3,4,4,4,3,NaN,1,4,1,0,1,0,0,NaN 146 | PG,2000,8,87,70,30,21,12,5,NaN,6,77,45,21,15,5,1,NaN 147 | PH,2000,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN 148 | PK,2000,55,498,387,256,232,153,130,NaN,130,591,416,274,163,103,56,NaN 149 | PL,2000,1,99,303,812,782,361,434,NaN,1,99,158,211,170,82,421,NaN 150 | PR,2000,0,1,4,19,9,10,14,NaN,1,4,5,3,7,1,3,NaN 151 | PS,2000,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN 152 | PT,2000,8,147,375,349,208,140,140,NaN,5,114,154,87,41,25,64,NaN 153 | PY,2000,16,112,103,105,86,80,71,NaN,12,69,86,41,41,30,46,NaN 154 | QA,2000,0,7,19,9,7,2,1,NaN,0,0,4,3,1,0,0,NaN 155 | RO,2000,46,832,1508,1799,1684,916,533,NaN,53,701,766,484,341,207,321,NaN 156 | RU,2000,1,295,526,596,402,151,54,NaN,1,43,73,74,38,31,44,NaN 157 | RW,2000,155,466,974,824,393,129,56,NaN,105,396,473,309,109,52,14,NaN 158 | SA,2000,0,131,268,213,158,86,107,NaN,28,172,182,79,51,50,70,NaN 159 | SB,2000,3,13,4,8,8,10,6,NaN,8,15,13,7,7,5,2,NaN 160 | SC,2000,NaN,NaN,2,4,1,1,NaN,NaN,NaN,NaN,1,0,1,1,NaN,NaN 161 | SD,2000,785,1028,1511,1351,1119,638,677,NaN,817,925,1134,905,771,327,323,NaN 162 | SE,2000,0,9,10,12,11,4,25,NaN,1,9,8,10,2,2,15,NaN 163 | SG,2000,1,8,9,34,51,26,64,NaN,1,9,8,7,9,5,16,NaN 164 | SI,2000,0,3,11,36,22,14,17,NaN,0,3,9,3,4,3,20,NaN 165 | SK,2000,2,6,15,31,50,16,32,NaN,0,5,9,7,5,4,54,NaN 166 | SL,2000,18,287,486,361,190,113,47,NaN,27,249,298,225,92,49,30,NaN 167 | SM,2000,NaN,NaN,NaN,NaN,NaN,NaN,1,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN 168 | SN,2000,60,772,1297,857,470,279,189,NaN,77,521,540,376,217,107,61,NaN 169 | SO,2000,113,740,724,408,254,195,142,NaN,85,354,319,219,110,72,41,NaN 170 | SR,2000,1,6,6,3,2,0,4,NaN,2,3,6,3,0,1,1,NaN 171 | ST,2000,1,5,11,4,7,3,10,NaN,3,7,15,5,7,4,15,NaN 172 | SV,2000,13,99,124,114,92,62,107,NaN,28,81,76,63,63,39,47,NaN 173 | SY,2000,8,359,289,125,86,76,55,NaN,23,195,101,53,46,38,28,NaN 174 | SZ,2000,11,130,352,249,138,37,17,NaN,10,198,298,62,62,24,5,NaN 175 | TC,2000,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN 176 | TG,2000,4,101,168,144,109,48,39,NaN,13,107,124,50,36,24,15,NaN 177 | TH,2000,27,859,2570,2380,2117,1908,2213,NaN,32,624,1035,780,873,1016,1321,NaN 178 | TJ,2000,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN 179 | TK,2000,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN 180 | TM,2000,16,103,185,144,127,31,21,NaN,19,73,140,76,31,34,17,NaN 181 | TN,2000,16,139,208,156,109,65,101,NaN,7,68,59,43,21,21,58,NaN 182 | TO,2000,NaN,2,1,1,NaN,1,5,NaN,NaN,1,1,1,NaN,1,1,NaN 183 | TR,2000,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN 184 | TT,2000,0,7,18,27,17,7,7,NaN,0,5,7,9,5,2,4,NaN 185 | TV,2000,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN 186 | TZ,2000,200,2357,4836,3430,2022,1202,834,NaN,257,2106,3426,1738,868,494,269,NaN 187 | UA,2000,21,693,1552,2385,2007,1062,532,NaN,41,487,590,447,298,218,405,NaN 188 | UG,2000,283,1511,3497,2479,1279,607,395,NaN,400,1649,2782,1510,671,316,163,NaN 189 | 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| -------------------------------------------------------------------------------- /data/uber1.csv: -------------------------------------------------------------------------------- 1 | Date,Time,Lat,Lon,Base 2 | 4/1/2014,0:11:00,40.769,-73.9549,B02512 3 | 4/1/2014,0:17:00,40.7267,-74.0345,B02512 4 | 4/1/2014,0:21:00,40.7316,-73.9873,B02512 5 | 4/1/2014,0:28:00,40.7588,-73.9776,B02512 6 | 4/1/2014,0:33:00,40.7594,-73.9722,B02512 7 | 4/1/2014,0:33:00,40.7383,-74.0403,B02512 8 | 4/1/2014,0:39:00,40.7223,-73.9887,B02512 9 | 4/1/2014,0:45:00,40.762,-73.979,B02512 10 | 4/1/2014,0:55:00,40.7524,-73.996,B02512 11 | 4/1/2014,1:01:00,40.7575,-73.9846,B02512 12 | 4/1/2014,1:19:00,40.7256,-73.9869,B02512 13 | 4/1/2014,1:48:00,40.7591,-73.9684,B02512 14 | 4/1/2014,1:49:00,40.7271,-73.9803,B02512 15 | 4/1/2014,2:11:00,40.6463,-73.7896,B02512 16 | 4/1/2014,2:25:00,40.7564,-73.9167,B02512 17 | 4/1/2014,2:31:00,40.7666,-73.9531,B02512 18 | 4/1/2014,2:43:00,40.758,-73.9761,B02512 19 | 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| 6/1/2014,0:37:00,40.7578,-73.9703,B02512 29 | 6/1/2014,0:39:00,40.7349,-73.985,B02512 30 | 6/1/2014,0:39:00,40.7133,-73.9775,B02512 31 | 6/1/2014,0:40:00,40.7273,-73.9936,B02512 32 | 6/1/2014,0:40:00,40.761,-73.6035,B02512 33 | 6/1/2014,0:44:00,40.7401,-73.9901,B02512 34 | 6/1/2014,0:46:00,40.7261,-73.9835,B02512 35 | 6/1/2014,0:48:00,40.7406,-74.0086,B02512 36 | 6/1/2014,0:49:00,40.6975,-73.9373,B02512 37 | 6/1/2014,0:53:00,40.7428,-73.9806,B02512 38 | 6/1/2014,0:55:00,40.7667,-74.2016,B02512 39 | 6/1/2014,0:59:00,40.7053,-73.9394,B02512 40 | 6/1/2014,0:59:00,40.7181,-73.6795,B02512 41 | 6/1/2014,1:00:00,40.7431,-73.9848,B02512 42 | 6/1/2014,1:00:00,40.7395,-74.0021,B02512 43 | 6/1/2014,1:01:00,40.7816,-73.7183,B02512 44 | 6/1/2014,1:01:00,40.8192,-73.9019,B02512 45 | 6/1/2014,1:02:00,40.7335,-74.0037,B02512 46 | 6/1/2014,1:04:00,40.7404,-74.0083,B02512 47 | 6/1/2014,1:09:00,40.796,-74.4726,B02512 48 | 6/1/2014,1:14:00,40.7397,-74.0081,B02512 49 | 6/1/2014,1:15:00,40.7233,-73.9855,B02512 50 | 6/1/2014,1:18:00,40.7535,-73.9851,B02512 51 | 6/1/2014,1:25:00,40.7978,-74.4777,B02512 52 | 6/1/2014,1:32:00,40.299,-73.9811,B02512 53 | 6/1/2014,1:37:00,40.7343,-74.166,B02512 54 | 6/1/2014,1:38:00,40.7392,-73.9953,B02512 55 | 6/1/2014,1:40:00,41.0169,-73.7182,B02512 56 | 6/1/2014,1:41:00,40.7196,-73.9995,B02512 57 | 6/1/2014,1:52:00,40.7398,-74.0054,B02512 58 | 6/1/2014,1:56:00,40.7295,-74.0084,B02512 59 | 6/1/2014,2:03:00,40.7408,-74.0079,B02512 60 | 6/1/2014,2:04:00,40.7381,-74.0089,B02512 61 | 6/1/2014,2:05:00,40.742,-74.0077,B02512 62 | 6/1/2014,2:15:00,40.6736,-73.992,B02512 63 | 6/1/2014,2:19:00,40.7195,-73.9887,B02512 64 | 6/1/2014,2:21:00,40.7144,-73.9651,B02512 65 | 6/1/2014,2:26:00,40.7621,-73.9747,B02512 66 | 6/1/2014,2:27:00,40.74,-74.0055,B02512 67 | 6/1/2014,2:32:00,40.762,-73.9936,B02512 68 | 6/1/2014,2:40:00,40.7175,-74.0055,B02512 69 | 6/1/2014,2:41:00,40.7521,-73.9954,B02512 70 | 6/1/2014,2:47:00,40.7222,-73.9588,B02512 71 | 6/1/2014,2:50:00,40.719,-73.9888,B02512 72 | 6/1/2014,2:51:00,40.6564,-73.6453,B02512 73 | 6/1/2014,2:53:00,40.7261,-73.9918,B02512 74 | 6/1/2014,2:54:00,40.7191,-73.9892,B02512 75 | 6/1/2014,2:58:00,40.7286,-73.9954,B02512 76 | 6/1/2014,3:10:00,40.7615,-73.975,B02512 77 | 6/1/2014,3:11:00,40.7278,-73.9991,B02512 78 | 6/1/2014,3:14:00,40.7503,-73.9839,B02512 79 | 6/1/2014,3:19:00,40.7389,-74.0081,B02512 80 | 6/1/2014,3:23:00,40.775,-73.9907,B02512 81 | 6/1/2014,3:32:00,40.7054,-74.0072,B02512 82 | 6/1/2014,3:35:00,40.7303,-74.0025,B02512 83 | 6/1/2014,3:36:00,40.7347,-73.9924,B02512 84 | 6/1/2014,4:03:00,40.7224,-73.9878,B02512 85 | 6/1/2014,4:51:00,40.6982,-73.462,B02512 86 | 6/1/2014,4:54:00,40.6449,-73.7818,B02512 87 | 6/1/2014,4:56:00,40.645,-73.782,B02512 88 | 6/1/2014,5:07:00,40.7699,-73.9843,B02512 89 | 6/1/2014,5:10:00,40.7372,-73.9967,B02512 90 | 6/1/2014,5:14:00,40.6448,-73.7826,B02512 91 | 6/1/2014,5:48:00,40.7228,-74.0019,B02512 92 | 6/1/2014,6:19:00,40.736,-73.9792,B02512 93 | 6/1/2014,6:21:00,40.7536,-73.9915,B02512 94 | 6/1/2014,6:24:00,40.7615,-73.9881,B02512 95 | 6/1/2014,6:25:00,40.8525,-73.8281,B02512 96 | 6/1/2014,6:27:00,40.7554,-73.9738,B02512 97 | 6/1/2014,6:35:00,40.7543,-73.9817,B02512 98 | 6/1/2014,6:37:00,40.7751,-73.9633,B02512 99 | 6/1/2014,6:46:00,40.6952,-74.1784,B02512 100 | 6/1/2014,6:51:00,40.7621,-73.9817,B02512 101 | -------------------------------------------------------------------------------- /functions/multilabel.py: -------------------------------------------------------------------------------- 1 | # from https://github.com/drivendataorg/box-plots-sklearn/blob/master/src/data/multilabel.py 2 | # these functions are used in 2024-01-19_school_budgeting_with_machine_learning_in_python 3 | 4 | from warnings import warn 5 | 6 | import numpy as np 7 | import pandas as pd 8 | 9 | def multilabel_sample(y, size=1000, min_count=5, seed=None): 10 | """ Takes a matrix of binary labels `y` and returns 11 | the indices for a sample of size `size` if 12 | `size` > 1 or `size` * len(y) if size =< 1. 13 | 14 | The sample is guaranteed to have > `min_count` of 15 | each label. 16 | """ 17 | try: 18 | if (np.unique(y).astype(int) != np.array([0, 1])).any(): 19 | raise ValueError() 20 | except (TypeError, ValueError): 21 | raise ValueError('multilabel_sample only works with binary indicator matrices') 22 | 23 | if (y.sum(axis=0) < min_count).any(): 24 | raise ValueError('Some classes do not have enough examples. Change min_count if necessary.') 25 | 26 | if size <= 1: 27 | size = np.floor(y.shape[0] * size) 28 | 29 | if y.shape[1] * min_count > size: 30 | msg = "Size less than number of columns * min_count, returning {} items instead of {}." 31 | warn(msg.format(y.shape[1] * min_count, size)) 32 | size = y.shape[1] * min_count 33 | 34 | rng = np.random.RandomState(seed if seed is not None else np.random.randint(1)) 35 | 36 | if isinstance(y, pd.DataFrame): 37 | choices = y.index 38 | y = y.values 39 | else: 40 | choices = np.arange(y.shape[0]) 41 | 42 | sample_idxs = np.array([], dtype=choices.dtype) 43 | 44 | # first, guarantee > min_count of each label 45 | for j in range(y.shape[1]): 46 | label_choices = choices[y[:, j] == 1] 47 | label_idxs_sampled = rng.choice(label_choices, size=min_count, replace=False) 48 | sample_idxs = np.concatenate([label_idxs_sampled, sample_idxs]) 49 | 50 | sample_idxs = np.unique(sample_idxs) 51 | 52 | # now that we have at least min_count of each, we can just random sample 53 | sample_count = int(size - sample_idxs.shape[0]) 54 | 55 | # get sample_count indices from remaining choices 56 | remaining_choices = np.setdiff1d(choices, sample_idxs) 57 | remaining_sampled = rng.choice(remaining_choices, 58 | size=sample_count, 59 | replace=False) 60 | 61 | return np.concatenate([sample_idxs, remaining_sampled]) 62 | 63 | 64 | def multilabel_sample_dataframe(df, labels, size, min_count=5, seed=None): 65 | """ Takes a dataframe `df` and returns a sample of size `size` where all 66 | classes in the binary matrix `labels` are represented at 67 | least `min_count` times. 68 | """ 69 | idxs = multilabel_sample(labels, size=size, min_count=min_count, seed=seed) 70 | return df.loc[idxs] 71 | 72 | 73 | def multilabel_train_test_split(X, Y, size, min_count=5, seed=None): 74 | """ Takes a features matrix `X` and a label matrix `Y` and 75 | returns (X_train, X_test, Y_train, Y_test) where all 76 | classes in Y are represented at least `min_count` times. 77 | """ 78 | index = Y.index if isinstance(Y, pd.DataFrame) else np.arange(Y.shape[0]) 79 | 80 | test_set_idxs = multilabel_sample(Y, size=size, min_count=min_count, seed=seed) 81 | train_set_idxs = np.setdiff1d(index, test_set_idxs) 82 | 83 | test_set_mask = index.isin(test_set_idxs) 84 | train_set_mask = ~test_set_mask 85 | 86 | return (X[train_set_mask], X[test_set_mask], Y[train_set_mask], Y[test_set_mask]) -------------------------------------------------------------------------------- /functions/score_sub.py: -------------------------------------------------------------------------------- 1 | # from https://goodboychan.github.io/python/datacamp/machine_learning/2020/06/05/01-School-Budgeting-with-Machine-Learning-in-Python.html 2 | # these functions are used in 2024-01-19_school_budgeting_with_maching_learning_in_python 3 | 4 | import pandas as pd 5 | import numpy as np 6 | 7 | 8 | BOX_PLOTS_COLUMN_INDICES = [range(0, 37), range(37, 48), range(48, 51), range(51, 76), range(76, 79), range(79, 82), range(82, 87), range(87, 96), range(96, 104)] 9 | 10 | 11 | def _multi_multi_log_loss(predicted, actual, class_column_indices=BOX_PLOTS_COLUMN_INDICES, eps=1e-15): 12 | """ Multi class version of Logarithmic Loss metric as implemented on 13 | DrivenData.org 14 | """ 15 | class_scores = np.ones(len(class_column_indices), dtype=np.float64) 16 | 17 | # calculate log loss for each set of columns that belong to a class: 18 | for k, this_class_indices in enumerate(class_column_indices): 19 | # get just the columns for this class 20 | preds_k = predicted[:, this_class_indices].astype(np.float64) 21 | 22 | # normalize so probabilities sum to one (unless sum is zero, then we clip) 23 | preds_k /= np.clip(preds_k.sum(axis=1).reshape(-1, 1), eps, np.inf) 24 | 25 | actual_k = actual[:, this_class_indices] 26 | 27 | # shrink predictions so 28 | y_hats = np.clip(preds_k, eps, 1 - eps) 29 | sum_logs = np.sum(actual_k * np.log(y_hats)) 30 | class_scores[k] = (-1.0 / actual.shape[0]) * sum_logs 31 | 32 | return np.average(class_scores) 33 | 34 | 35 | def score_submission(pred_path='./', holdout_path='https://s3.amazonaws.com/assets.datacamp.com/production/course_2826/datasets/TestSetLabelsSample.csv'): 36 | # this happens on the backend to get the score 37 | holdout_labels = pd.get_dummies( 38 | pd.read_csv(holdout_path, index_col=0) 39 | .apply(lambda x: x.astype('category'), axis=0) 40 | ) 41 | 42 | preds = pd.read_csv(pred_path, index_col=0) 43 | 44 | # make sure that format is correct 45 | # assert (preds.columns == holdout_labels.columns).all() 46 | # assert (preds.index == holdout_labels.index).all() 47 | 48 | return _multi_multi_log_loss(preds.values, holdout_labels.values) -------------------------------------------------------------------------------- /functions/sparse_interactions.py: -------------------------------------------------------------------------------- 1 | from itertools import combinations 2 | 3 | import numpy as np 4 | from scipy import sparse 5 | from sklearn.base import BaseEstimator, TransformerMixin 6 | 7 | 8 | class SparseInteractions(BaseEstimator, TransformerMixin): 9 | def __init__(self, degree=2, feature_name_separator="_"): 10 | self.degree = degree 11 | self.feature_name_separator = feature_name_separator 12 | 13 | def fit(self, X, y=None): 14 | return self 15 | 16 | def transform(self, X): 17 | if not sparse.isspmatrix_csc(X): 18 | X = sparse.csc_matrix(X) 19 | 20 | if hasattr(X, "columns"): 21 | self.orig_col_names = X.columns 22 | else: 23 | self.orig_col_names = np.array([str(i) for i in range(X.shape[1])]) 24 | 25 | spi = self._create_sparse_interactions(X) 26 | return spi 27 | 28 | def get_feature_names(self): 29 | return self.feature_names 30 | 31 | def _create_sparse_interactions(self, X): 32 | out_mat = [] 33 | self.feature_names = self.orig_col_names.tolist() 34 | 35 | for sub_degree in range(2, self.degree + 1): 36 | for col_ixs in combinations(range(X.shape[1]), sub_degree): 37 | # add name for new column 38 | name = self.feature_name_separator.join(self.orig_col_names[list(col_ixs)]) 39 | self.feature_names.append(name) 40 | 41 | # get column multiplications value 42 | out = X[:, col_ixs[0]] 43 | for j in col_ixs[1:]: 44 | out = out.multiply(X[:, j]) 45 | 46 | out_mat.append(out) 47 | 48 | return sparse.hstack([X] + out_mat) -------------------------------------------------------------------------------- /python_data_science_toolbox_1.py: -------------------------------------------------------------------------------- 1 | import pandas as pd 2 | import csv 3 | from pprint import pprint as pp 4 | from functools import reduce 5 | 6 | 7 | def read_out_csv(): 8 | with open('tweets.csv') as fp: 9 | reader = csv.reader(fp) 10 | for row in reader: 11 | print(row) 12 | 13 | 14 | def make_df(file_name): 15 | return pd.read_csv(file_name) 16 | 17 | 18 | def square(value: float) -> float: # Function Header with parameters and type hints 19 | """ 20 | Return the square of a value 21 | :param value: float 22 | :return: float 23 | """ 24 | new_value = value ** 2 25 | print(new_value) # Function Body 26 | return new_value 27 | 28 | 29 | in_num = 3.0 30 | num = square(in_num) 31 | print('\nOutput from the function square:') 32 | print(f'The square of {in_num} is {num} and its type is {type(num)}\n') 33 | 34 | 35 | def raise_to_power(value1: float, value2: float) -> tuple: 36 | """ 37 | Raise value1 to the power of value2 and vise versa 38 | :param value1: float 39 | :param value2: float 40 | :return: tuple of floats 41 | """ 42 | new_value1 = value1 ** value2 43 | new_value2 = value2 ** value1 44 | return new_value1, new_value2 45 | 46 | 47 | in_num = [10.0, 40.5] 48 | num1, num2 = raise_to_power(in_num[0], in_num[1]) 49 | print('\nOutput from the function raise_to_power:') 50 | print(f'Value1^Value2: {num1}\nValue2^Value1: {num2}\n') 51 | 52 | 53 | def tweets(): 54 | # Import Twitter data as DataFrame: df 55 | df = pd.read_csv('tweets.csv') 56 | 57 | # Initialize an empty dictionary: langs_count 58 | langs_count = {} 59 | 60 | # Extract column from DataFrame: col 61 | col = df['lang'] 62 | 63 | # Iterate over lang column in DataFrame 64 | for entry in col: 65 | 66 | # If the language is in langs_count, add 1 67 | if entry in langs_count.keys(): 68 | langs_count[entry] = langs_count[entry] + 1 69 | # Else add the language to langs_count, set the value to 1 70 | else: 71 | langs_count[entry] = 1 72 | 73 | # Print the populated dictionary 74 | print(langs_count) 75 | 76 | 77 | print('\nOutput from the function tweets:') 78 | tweets() 79 | 80 | 81 | def count_entries(df, col_name): 82 | """Return a dictionary with counts of 83 | occurrences as value for each key.""" 84 | 85 | # Initialize an empty dictionary: langs_count 86 | langs_count = {} 87 | 88 | # Extract column from DataFrame: col 89 | col = df[col_name] 90 | 91 | # Iterate over lang column in DataFrame 92 | for entry in col: 93 | 94 | # If the language is in langs_count, add 1 95 | if entry in langs_count.keys(): 96 | langs_count[entry] = langs_count[entry] + 1 97 | # Else add the language to langs_count, set the value to 1 98 | else: 99 | langs_count[entry] = 1 100 | 101 | # Return the langs_count dictionary 102 | return langs_count 103 | 104 | 105 | # Call count_entries(): result 106 | result = count_entries(make_df('tweets.csv'), 'lang') 107 | 108 | # Print the result 109 | print('\nOutput from the function count_entries:') 110 | print(result) 111 | 112 | 113 | def count_entries2(df, col_name='lang'): 114 | """Return a dictionary with counts of 115 | occurrences as value for each key.""" 116 | 117 | # Initialize an empty dictionary: cols_count 118 | cols_count = {} 119 | 120 | # Extract column from DataFrame: col 121 | col = df[col_name] 122 | 123 | # Iterate over the column in DataFrame 124 | for entry in col: 125 | 126 | # If entry is in cols_count, add 1 127 | if entry in cols_count.keys(): 128 | cols_count[entry] += 1 129 | 130 | # Else add the entry to cols_count, set the value to 1 131 | else: 132 | cols_count[entry] = 1 133 | 134 | # Return the cols_count dictionary 135 | return cols_count 136 | 137 | 138 | tweets_df = make_df('tweets.csv') 139 | 140 | # Call count_entries(): result1 141 | result1 = count_entries2(tweets_df) 142 | 143 | # Call count_entries(): result2 144 | result2 = count_entries2(tweets_df, col_name='source') 145 | 146 | # Print result1 and result2 147 | print('\nOutput from the function count_entries2:') 148 | print('Result1:') 149 | pp(result1) 150 | print('Result2:') 151 | pp(result2) 152 | 153 | 154 | def count_entries3(df, *args): 155 | """Return a dictionary with counts of 156 | occurrences as value for each key.""" 157 | 158 | # Initialize an empty dictionary: cols_count 159 | cols_count = {} 160 | 161 | # Iterate over column names in args 162 | for col_name in args: 163 | 164 | # Extract column from DataFrame: col 165 | col = df[col_name] 166 | 167 | # Iterate over the column in DataFrame 168 | for entry in col: 169 | 170 | # If entry is in cols_count, add 1 171 | if entry in cols_count.keys(): 172 | cols_count[entry] += 1 173 | 174 | # Else add the entry to cols_count, set the value to 1 175 | else: 176 | cols_count[entry] = 1 177 | 178 | # Return the cols_count dictionary 179 | return cols_count 180 | 181 | 182 | # Call count_entries(): result1 183 | result1 = count_entries3(tweets_df, 'lang') 184 | 185 | # Call count_entries(): result2 186 | result2 = count_entries3(tweets_df, 'lang', 'source') 187 | 188 | # Print result1 and result2 189 | print('\nOutput from the function count_entries3:') 190 | print('Result1:') 191 | pp(result1) 192 | print('Result2:') 193 | pp(result2) 194 | print('\n') 195 | 196 | 197 | # Lambda Functions 198 | print('\n') 199 | print('Lambda Functions: https://www.geeksforgeeks.org/python-lambda-anonymous-functions-filter-map-reduce/') 200 | print('\n') 201 | 202 | 203 | def ex_1(): 204 | nums = [48, 6, 9, 21, 1] 205 | square_all = map(lambda num: num ** 2, nums) 206 | print('Using Lambda Functions:') 207 | print(square_all) 208 | print(list(square_all)) 209 | print('\n') 210 | 211 | 212 | ex_1() 213 | 214 | 215 | def ex_2(): 216 | add_bangs = (lambda a: a + '!!!') 217 | print(add_bangs('hello')) 218 | 219 | 220 | ex_2() 221 | 222 | 223 | def ex_3(): 224 | # Define echo_word as a lambda function: echo_word 225 | echo_word = (lambda word1, echo: word1 * echo) 226 | 227 | # Call echo_word: result 228 | result_ex_3 = echo_word('hey', 5) 229 | 230 | # Print result 231 | print('\nResult ex_3:') 232 | print(result_ex_3) 233 | 234 | 235 | ex_3() 236 | 237 | 238 | def ex_4(): 239 | # Create a list of strings: spells 240 | spells = ["protego", "accio", "expecto patronum", "legilimens"] 241 | 242 | # Use map() to apply a lambda function over spells: shout_spells 243 | shout_spells = map(lambda x: x + '!!!', spells) 244 | 245 | # Convert shout_spells to a list: shout_spells_list 246 | shout_spells_list = list(shout_spells) 247 | 248 | # Convert shout_spells into a list and print it 249 | print('\nResult ex_4: shout_spells') 250 | print(shout_spells_list) 251 | 252 | 253 | ex_4() 254 | 255 | 256 | def ex_5(): 257 | # Create a list of strings: fellowship 258 | fellowship = ['frodo', 'samwise', 'merry', 'aragorn', 'legolas', 'boromir', 'gimli'] 259 | 260 | # Use filter() to apply a lambda function over fellowship: result 261 | result_ex_5 = filter(lambda x: (len(x) > 6), fellowship) 262 | 263 | # Convert result to a list: result_list 264 | result_list = list(result_ex_5) 265 | 266 | # Convert result into a list and print it 267 | print('\nResult ex_5: fellowship') 268 | print(result_list) 269 | 270 | 271 | ex_5() 272 | 273 | 274 | def ex_6(): 275 | # Import reduce from functools -> above 276 | 277 | # Create a list of strings: stark 278 | stark = ['robb', 'sansa', 'arya', 'eddard', 'jon'] 279 | 280 | # Use reduce() to apply a lambda function over stark: result 281 | result_ex_6 = reduce((lambda item1, item2: (item1 + item2)), stark) 282 | 283 | # Print the result 284 | print('\nResult ex_6: starks') 285 | print(result_ex_6) 286 | 287 | 288 | ex_6() 289 | 290 | 291 | # Error Handling 292 | print('\nError Handling:\n') 293 | 294 | 295 | def shout_echo(word1, echo=1): 296 | """Concatenate echo copies of word1 and three 297 | exclamation marks at the end of the string.""" 298 | 299 | # Raise an error with raise 300 | if echo < 0: 301 | raise ValueError('echo must be greater than 0') 302 | 303 | # Concatenate echo copies of word1 using *: echo_word 304 | echo_word = word1 * echo 305 | 306 | # Concatenate '!!!' to echo_word: shout_word 307 | shout_word = echo_word + '!!!' 308 | 309 | # Return shout_word 310 | return shout_word 311 | 312 | 313 | # Call shout_echo 314 | print('\nResult shout_echo') 315 | result_shout_echo = shout_echo("particle", echo=5) 316 | print(result_shout_echo) 317 | 318 | 319 | # Bring it all together 320 | print('\nBring it all together!\n') 321 | 322 | 323 | def ex_7(df): 324 | # Select retweets from the Twitter DataFrame: result 325 | result_ex_7 = filter(lambda x: x[0:2] == 'RT', df['text']) 326 | 327 | # Create list from filter object result: res_list 328 | res_list = list(result_ex_7) 329 | 330 | # Print all retweets in res_list 331 | for tweet in res_list: 332 | print(tweet) 333 | 334 | 335 | print('\nResults of ex_7') 336 | ex_7(tweets_df) 337 | 338 | 339 | def count_entries_4(df, col_name='lang'): 340 | """Return a dictionary with counts of 341 | occurrences as value for each key.""" 342 | 343 | # Initialize an empty dictionary: cols_count 344 | cols_count = {} 345 | 346 | # Add try block 347 | try: 348 | # Extract column from DataFrame: col 349 | col = df[col_name] 350 | 351 | # Iterate over the column in dataframe 352 | for entry in col: 353 | 354 | # If entry is in cols_count, add 1 355 | if entry in cols_count.keys(): 356 | cols_count[entry] += 1 357 | # Else add the entry to cols_count, set the value to 1 358 | else: 359 | cols_count[entry] = 1 360 | 361 | # Return the cols_count dictionary 362 | return cols_count 363 | 364 | # Add except block 365 | except KeyError: 366 | print('The DataFrame does not have a ' + col_name + ' column.') 367 | 368 | 369 | print('\nResults of count_entries_4') 370 | # Call count_entries(): result1 371 | result1 = count_entries_4(tweets_df, 'lang') 372 | 373 | # Print result1 374 | print(result1) 375 | 376 | # Call count_entries(): result2 377 | result2 = count_entries_4(tweets_df, 'lang1') 378 | print(result2) 379 | 380 | 381 | def count_entries_5(df, col_name='lang'): 382 | """Return a dictionary with counts of 383 | occurrences as value for each key.""" 384 | 385 | # Raise a ValueError if col_name is NOT in DataFrame 386 | if col_name not in df.columns: 387 | raise ValueError('The DataFrame does not have a ' + col_name + ' column.') 388 | 389 | # Initialize an empty dictionary: cols_count 390 | cols_count = {} 391 | 392 | # Extract column from DataFrame: col 393 | col = df[col_name] 394 | 395 | # Iterate over the column in DataFrame 396 | for entry in col: 397 | 398 | # If entry is in cols_count, add 1 399 | if entry in cols_count.keys(): 400 | cols_count[entry] += 1 401 | # Else add the entry to cols_count, set the value to 1 402 | else: 403 | cols_count[entry] = 1 404 | 405 | # Return the cols_count dictionary 406 | return cols_count 407 | 408 | 409 | print('\nResults of count_entries_5') 410 | # Call count_entries(): result1 411 | result1 = count_entries_5(tweets_df) 412 | print(result1) 413 | -------------------------------------------------------------------------------- /snakes.json: -------------------------------------------------------------------------------- 1 | {"Actors": "Samuel L. Jackson, Julianna Margulies, Nathan Phillips, Rachel Blanchard", 2 | "Awards": "3 wins & 7 nominations.", 3 | "Country": "Germany, USA, Canada", 4 | "Director": "David R. Ellis", 5 | "Genre": "Action, Adventure, Crime", 6 | "Language": "English", 7 | "Rated": "R", 8 | "Released": "18 Aug 2006", 9 | "Runtime": "105 min", 10 | "Title": "Snakes on a Plane", 11 | "Type": "movie", 12 | "Writer": "John Heffernan (screenplay), Sebastian Gutierrez (screenplay), David Dalessandro (story), John Heffernan (story)", 13 | "Year": "2006", 14 | "imdbID": "tt0417148", 15 | "imdbRating": "5.6", 16 | "imdbVotes": "114,668"} -------------------------------------------------------------------------------- /testing.py: -------------------------------------------------------------------------------- 1 | import pandas as pd 2 | import numpy as np 3 | 4 | 5 | def exer_1(): 6 | logins = {'MONTH': ['JAN', 'FEB'], 7 | 'day': [7, 8], 8 | 'year': [2015, 2015], 9 | 'session_id': [17357, 10011]} 10 | 11 | logins_df = pd.DataFrame(logins) 12 | print(logins_df, '\n') 13 | 14 | # add 'month' to the DataFrame and give it the value of 'MONTH' at index j 15 | for j, p in logins_df.iterrows(): 16 | logins_df.loc[j, 'month'] = p['MONTH'].lower() 17 | 18 | print(logins_df) 19 | 20 | 21 | print('Output of exer_1:') 22 | exer_1() 23 | 24 | 25 | def exer_2(): 26 | marketing = {'ID': [1, 2, 3, 4], 27 | 'Views': [1000, 1200, 800, 1500], 28 | 'Clicks': [300, 800, 500, 990]} 29 | 30 | marketing_df = pd.DataFrame(marketing) 31 | 32 | print(marketing_df) 33 | 34 | 35 | print('\nOutput of exer_2:') 36 | exer_2() 37 | 38 | 39 | def exer_3(): 40 | group = ['fruit', 'fruit', 'fruit', 'vegetable', 'vegetable', 'vegetable'] 41 | name = ['apple', 'banana', 'oragne', 'broccoli', 'kale', 'lettuce'] 42 | count = [90, 150, 130, 80, 70, 125] 43 | prod_dict = {'group': group, 'name': name, 'count': count} 44 | df = pd.DataFrame(prod_dict) 45 | print(df, '\n') 46 | print(df[np.invert(df['group'] == 'fruit')]) 47 | 48 | 49 | print('\nOutput of exer_3:') 50 | exer_3() 51 | 52 | 53 | def nth_root(n): 54 | """Returns the actual_root function""" 55 | def actual_root(x): 56 | """Returns the nth root of x""" 57 | root = x ** (1/n) 58 | return root 59 | return actual_root 60 | 61 | 62 | print('\nOutput of nth_root:') 63 | square_root = nth_root(2) 64 | cube_root = nth_root(3) 65 | print(square_root(16), cube_root(27)) 66 | 67 | 68 | def easy_print(**kwargs): 69 | for p, q in kwargs.items(): 70 | print('The value of ' + str(p) + " is " + str(q)) 71 | 72 | 73 | print('\nResult of easy_print') 74 | easy_print(x=10, y=20) 75 | 76 | 77 | def add_zeros(string): 78 | """Returns a string padded with zeros to ensure consistent length""" 79 | updated_string = string + '0' 80 | 81 | def add_more(): 82 | """Adds more zeros if necessary""" 83 | nonlocal updated_string 84 | updated_string = updated_string + '0' 85 | 86 | while len(updated_string) < 6: 87 | add_more() 88 | return updated_string 89 | 90 | 91 | print('\nResult of add_zeros: nonlocal variable updates') 92 | (add_zeros('2.3'), add_zeros('5.678')) 93 | -------------------------------------------------------------------------------- /twitter.py: -------------------------------------------------------------------------------- 1 | import tweepy 2 | import json 3 | 4 | 5 | class MyStreamListener(tweepy.StreamListener): 6 | def __init__(self, api=None): 7 | super(MyStreamListener, self).__init__() 8 | self.num_tweets = 0 9 | self.file = open("tweets.txt", "w") 10 | 11 | def on_status(self, status): 12 | tweet = status._json 13 | self.file.write(json.dumps(tweet) + '\n') 14 | self.num_tweets += 1 15 | if self.num_tweets < 1000: 16 | return True 17 | else: 18 | return False 19 | self.file.close() 20 | 21 | def on_error(self, status): 22 | print(status) --------------------------------------------------------------------------------