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| _Solutions.ipynb |
307.21Кб |
| 01. Automatic Index Alignment.ipynb |
13.89Кб |
| 01. Datetime and Timedelta.ipynb |
21.78Кб |
| 01. Grouping Aggregation Basics.ipynb |
18.01Кб |
| 01. Integer, Float, and Boolean Data types.ipynb |
40.02Кб |
| 01. Introduction to DataFrames.ipynb |
18.85Кб |
| 01. Introduction to matplotlib.ipynb |
29.15Кб |
| 01. Introduction to Regular Expressions.ipynb |
12.35Кб |
| 01. Plotting with pandas Series.ipynb |
15.33Кб |
| 01. Selecting Subsets of Data from DataFrames with just the brackets.ipynb |
12.44Кб |
| 01. Series Attributes and Statistical Methods.ipynb |
30.64Кб |
| 01. Tidy Data with melt.ipynb |
9.25Кб |
| 01. What is Pandas.ipynb |
24.86Кб |
| 02. Combining Data.ipynb |
4.82Кб |
| 02. DataFrame Statistical Methods.ipynb |
27.09Кб |
| 02. Grouping and Aggregating with Multiple Columns.ipynb |
14.00Кб |
| 02. Introduction to Time Series.ipynb |
14.09Кб |
| 02. Matplotlib Text and Lines.ipynb |
33.01Кб |
| 02. Object, String, and Categorical Data Types.ipynb |
44.59Кб |
| 02. Plotting with pandas DataFrames.ipynb |
30.30Кб |
| 02. Quantifiers.ipynb |
6.73Кб |
| 02. Reshaping by Pivoting.ipynb |
10.05Кб |
| 02. Selecting Subsets of Data from DataFrames with loc.ipynb |
18.01Кб |
| 02. Series Missing Value Methods.ipynb |
20.81Кб |
| 02. The DataFrame and Series.ipynb |
20.07Кб |
| 03. Common Messy Datasets.ipynb |
15.29Кб |
| 03. DataFrame Missing Value Methods.ipynb |
18.52Кб |
| 03. Data Types and Missing Values.ipynb |
13.44Кб |
| 03. Datetime, Timedelta, and Period Data Types.ipynb |
21.87Кб |
| 03. Grouping by Time.ipynb |
15.01Кб |
| 03. Grouping with Pivot Tables.ipynb |
25.15Кб |
| 03. Matplotlib Resolution.ipynb |
28.62Кб |
| 03. Or Conditions.ipynb |
8.28Кб |
| 03. Seaborn Axes Plots.ipynb |
58.65Кб |
| 03. Selecting Subsets of Data from DataFrames with iloc.ipynb |
13.24Кб |
| 03. Series Sorting, Ranking, and Uniqueness.ipynb |
21.43Кб |
| 03. SQL Databases.ipynb |
10.87Кб |
| 04. Character Sets and Grouping.ipynb |
17.07Кб |
| 04. Counting with Crosstabs.ipynb |
12.30Кб |
| 04. DataFrame Data Type Conversion.ipynb |
13.37Кб |
| 04. DataFrame Sorting, Ranking, and Uniqueness.ipynb |
16.28Кб |
| 04. Data Normalization.ipynb |
12.66Кб |
| 04. Matplotlib Patches and Colors.ipynb |
51.27Кб |
| 04. Rolling Windows.ipynb |
8.60Кб |
| 04. Seaborn Grid Plots.ipynb |
24.31Кб |
| 04. Selecting Subsets of Data from a Series.ipynb |
13.50Кб |
| 04. Series Methods More.ipynb |
18.18Кб |
| 04. Setting a Meaningful Index.ipynb |
16.64Кб |
| 05. Alternate Groupby Syntax.ipynb |
5.72Кб |
| 05. Boolean Selection Single Conditions.ipynb |
15.77Кб |
| 05. DataFrame Structure Methods.ipynb |
18.89Кб |
| 05. Five-Step Process for Data Exploration.ipynb |
9.71Кб |
| 05. Grouping by Time and another Column.ipynb |
7.55Кб |
| 05. Matplotlib Line Plots.ipynb |
35.83Кб |
| 05. Project - Explore Newsgroups with Regexes.ipynb |
2.49Кб |
| 05. Series String Methods.ipynb |
18.18Кб |
| 06. Boolean Selection Multiple Conditions.ipynb |
16.11Кб |
| 06. Custom Aggregation.ipynb |
32.83Кб |
| 06. DataFrame Methods More.ipynb |
16.61Кб |
| 06. Matplotlib Scatter and Bar Plots.ipynb |
40.56Кб |
| 06. Project - Feature Engineering on the Titanic.ipynb |
6.10Кб |
| 06. Series Datetime Methods.ipynb |
15.68Кб |
| 07. Assigning Subsets of Data.ipynb |
10.17Кб |
| 07. Boolean Selection More.ipynb |
14.34Кб |
| 07. Matplotlib Distribution Plots.ipynb |
19.57Кб |
| 07. Transform and Filter with Groupby.ipynb |
25.28Кб |
| 08. Best of the Rest of Matplotlib.ipynb |
57.45Кб |
| 08. Filtering with the query Method.ipynb |
16.01Кб |
| 08. Other Groupby Methods.ipynb |
15.45Кб |
| 09. Create Your Own Data Analysis.ipynb |
4.88Кб |
| 09. Miscellaneous Subset Selection.ipynb |
10.86Кб |
| 1.1 Selecting Subsets of DataFrames with Just the Brackets.mkv |
41.48Мб |
| 1.1 Series Attributes and Methods.mkv |
86.50Мб |
| 1.2 Exercise Solutions.mkv |
29.31Мб |
| 1.2 Exercise Solutions.mkv |
41.30Мб |
| 1. Exploring the Course Contents.mp4 |
27.38Мб |
| 1. What is Pandas.mkv |
25.98Мб |
| 10.1 Numeric and Boolean Data Types.mkv |
76.42Мб |
| 10.2 Object Data Types.mkv |
21.83Мб |
| 10.3 Datetime64 Data Type.mkv |
42.92Мб |
| 10.4 Converting Strings to Numeric.mkv |
24.32Мб |
| 10.5 DataFrame Data Type Conversion.mkv |
41.41Мб |
| 10.6 Reading in Data with Known Missing Values.mkv |
17.15Мб |
| 10.7 Timedelta64 Data Type.mkv |
22.62Мб |
| 10.8 Data Type Summary Table.mkv |
31.00Мб |
| 10.9 Exercise Solutions.mkv |
78.04Мб |
| 11.1 Assigning Subsets with loc and iloc.mkv |
27.58Мб |
| 11.2 Boolean Selection Assignment.mkv |
55.51Мб |
| 11.3 Exercise Solutions.mkv |
27.90Мб |
| 12. Case Study - Calculating Normality of Stock Market Returns.mkv |
79.47Мб |
| 2.1 Selecting Subsets of Data from DataFrames with loc.mkv |
55.49Мб |
| 2.1 Series Methods More.mkv |
51.21Мб |
| 2.2 Exercise Solutions.mkv |
44.99Мб |
| 2.2 Exercise Solutions.mkv |
21.72Мб |
| 2. Opening the Material with Jupyter Notebooks.mkv |
58.70Мб |
| 2. Pandas Examples.mp4 |
48.97Мб |
| 3.1 Selecting Subsets of Data with iloc.mkv |
34.38Мб |
| 3.1 Series Methods More II.mkv |
68.53Мб |
| 3.2 Exercise Solutions.mkv |
20.81Мб |
| 3.2 Exercise Solutions.mkv |
27.66Мб |
| 3. Jupyter Notebook Tips and Tricks.mp4 |
21.90Мб |
| 3. The DataFrame and Series.mkv |
73.02Мб |
| 4.1 Selecting Subsets of Data from a Series.mkv |
54.85Мб |
| 4.1 String Series Methods.mkv |
58.71Мб |
| 4.2 Exercise Solutions.mkv |
33.31Мб |
| 4.2 Exercise Solutions.mkv |
57.83Мб |
| 4. Exercise Solutions - The DataFrame and Series.mkv |
25.20Мб |
| 4. Working through a Notebook from the Course.mkv |
51.03Мб |
| 5.1 Boolean Indexing Single Conditions.mkv |
49.82Мб |
| 5.1 Datetime Series Methods.mkv |
77.44Мб |
| 5.2 Exercise Solutions.mkv |
25.50Мб |
| 5.2 Exercise Solutions.mkv |
36.52Мб |
| 5. About to Begin.mkv |
22.97Мб |
| 5. Data Types and Missing Values.mkv |
68.50Мб |
| 6.1 Boolean Indexing Multiple Conditions.mkv |
53.16Мб |
| 6.1 Dataframe Attributes and Methods.mkv |
87.26Мб |
| 6.2 Exercise Solutions.mkv |
64.76Мб |
| 6.2 Exercise Solutions.mkv |
19.27Мб |
| 6. Exercise Solutions - Data Types and Missing Values.mkv |
31.62Мб |
| 7.1 Boolean Indexing More.mkv |
86.62Мб |
| 7.1 DataFrame Aggregation Methods.mkv |
57.61Мб |
| 7.2 DataFrame Non-Aggregation Methods.mkv |
37.66Мб |
| 7.2 Exercise Solutions.mkv |
57.78Мб |
| 7.3 Nuisance Columns.mkv |
67.79Мб |
| 7.4 Exercise Solutions.mkv |
62.17Мб |
| 7. Five-Step Process for Data Exploration.mkv |
70.13Мб |
| 8.1 DataFrame Methods More - Handling Missing Data.mkv |
68.08Мб |
| 8.1 Miscellaneous Subset Selection.mkv |
86.23Мб |
| 8.2 DataFrame Methods More 2 Sorting.mkv |
41.29Мб |
| 8.2 Exercise Solutions.mkv |
42.19Мб |
| 8.3 Finding the index of the maximum or minimum.mkv |
51.21Мб |
| 8.4 Dropping and Renaming Columns and Rows.mkv |
27.97Мб |
| 8.5 Adding New Columns.mkv |
36.68Мб |
| 8.6 Exercise Solutions.mkv |
98.84Мб |
| 9.1 DataFrame Methods more II.mkv |
61.10Мб |
| 9.2 The copy method.mkv |
18.85Мб |
| 9.3 Inserting and Popping Columns.mkv |
35.89Мб |
| 9.4 The replace Method.mkv |
32.27Мб |
| 9.5 Finding the Maximum per Group.mkv |
32.95Мб |
| 9.6 Exercise Solutions.mkv |
80.34Мб |
| aapl_sample.csv |
114б |
| airbnb.csv |
859.25Кб |
| all_colormaps.png |
93.30Кб |
| all_colors.png |
150.85Кб |
| amzn_sample.csv |
114б |
| arrowstyle.csv |
626б |
| average_arrival_delay.csv |
622б |
| ballmer.png |
51.67Кб |
| beer.csv |
3.14Мб |
| bikes.csv |
7.18Мб |
| blackhole.png |
41.84Кб |
| chinook_er.jpg |
77.10Кб |
| chinook.db |
864.00Кб |
| clean_movie1.csv |
536б |
| clean_movie2.csv |
545б |
| college_data_dictionary.csv |
1.00Кб |
| college.csv |
1.19Мб |
| colors.csv |
127б |
| connectionstyle.csv |
202б |
| country_hour_price.csv |
145б |
| dataframe_axes.png |
199.97Кб |
| Data Scientist Challenge.ipynb |
3.92Кб |
| Data Scientist Challenge Solution.ipynb |
882.16Кб |
| datetime_dtypes.png |
68.29Кб |
| dept_race_mean_max_axis1.png |
53.09Кб |
| dept_race_mean_max_gradient.png |
75.89Кб |
| dept_race_mean_max.png |
50.37Кб |
| df_agg_keep_dim.png |
26.75Кб |
| df_axes_explanation.png |
231.15Кб |
| df_components.png |
142.08Кб |
| diamonds_dictionary.csv |
545б |
| diamonds.csv |
2.45Мб |
| doctor_data_model.png |
46.05Кб |
| doctor_visits.csv |
759б |
| double_bracket_selection.png |
18.36Кб |
| dtypes_summary.png |
45.26Кб |
| employee_messy1.csv |
94б |
| employee_messy2.csv |
405б |
| employee_salary_stats.csv |
426б |
| employee.csv |
1.46Мб |
| energy_by_sector.xlsx |
62.65Кб |
| energy_consumption.csv |
68.68Кб |
| extra mpl.ipynb |
7.83Кб |
| fig_ax.png |
22.57Кб |
| findrc.png |
82.29Кб |
| flight_status.csv |
1.20Кб |
| flights_old.csv |
15.85Мб |
| flights.csv |
3.50Мб |
| full_covid_data.csv |
208.33Кб |
| genetic_engineered.xls |
121.50Кб |
| girl_height.csv |
220б |
| group_aggregate.png |
242.31Кб |
| Grouping with Continuous Variables.ipynb |
725б |
| heart_data_dictionary.csv |
659б |
| heart.csv |
22.33Кб |
| housing_data_dictionary.txt |
13.06Кб |
| housing.csv |
449.88Кб |
| Impaired_Driving_Death_Rate.csv |
5.41Кб |
| insurance.csv |
54.32Кб |
| Introduction to Jupyter Notebooks.pdf |
801.57Кб |
| Introduction to Jupyter Notebooks.pdf |
2.72Мб |
| Jupyter Notebooks.rar |
961.50Кб |
| just_cols.png |
58.83Кб |
| just_cols2.png |
29.67Кб |
| just_rows.png |
52.09Кб |
| just_rows2.png |
20.20Кб |
| library_data_dictionary.csv |
1.02Кб |
| library.csv |
1.32Мб |
| LICENSE |
875б |
| LICENSE |
875б |
| LICENSE |
881б |
| life_expectancy.csv |
239.74Кб |
| line_options.csv |
1.76Кб |
| line_styles.csv |
545б |
| Master Data Analysis with Python.pdf |
280.27Мб |
| Master Data Analysis with Python Solutions.pdf |
65.39Мб |
| matplotlib_measure.png |
296.87Кб |
| Matplotlib Cheatsheet.ipynb |
324.50Кб |
| mbk_tidy.csv |
12.58Кб |
| mdap.mplstyle |
110б |
| meetup.csv |
100.76Кб |
| member_groups.csv |
186.48Кб |
| member_info.csv |
276.50Кб |
| mental_health_dd.csv |
1.32Кб |
| mental_health.csv |
125.89Кб |
| metrics.csv |
351б |
| Mini Web App Finding Similar Members with the Meetup API.ipynb |
481.81Кб |
| missing_example.csv |
60б |
| movie.csv |
1.12Мб |
| mpl_coordinate_system.png |
95.29Кб |
| msft_sample.csv |
649б |
| msft20.csv |
277.85Кб |
| my_brothers_keeper.csv |
3.74Кб |
| nadella.png |
202.96Кб |
| named_colors.png |
146.58Кб |
| nba_court.png |
111.36Кб |
| neurIPS.db |
13.87Мб |
| new_deaths_tidy.csv |
174.49Кб |
| new_deaths.csv |
38.89Кб |
| newsgroups.csv |
731.78Кб |
| niko.png |
9.48Мб |
| nikosaved.png |
310.89Кб |
| nyc_deaths.csv |
24.91Кб |
| obj_str_cat_dtypes.png |
59.99Кб |
| offsetalias.png |
115.67Кб |
| orders.csv |
250.12Кб |
| pandas_logo.png |
32.24Кб |
| pandas_number_dtypes.png |
149.07Кб |
| pandas_numpy_dtypes.png |
68.26Кб |
| pandas_numpy_other.png |
40.00Кб |
| pandas_only_dtypes.png |
119.60Кб |
| Pandas Cheat Sheet.ipynb |
37.92Кб |
| penelope.png |
213.39Кб |
| pivot_table_example.png |
27.04Кб |
| pixel_measure.png |
86.07Кб |
| planecrashinfo.csv |
2.13Мб |
| planecrashinfo.csv |
2.08Мб |
| Project - Testing Normality of Stock Market Returns.ipynb |
13.21Кб |
| pyplot_dir.png |
98.37Кб |
| Python Installation.pdf |
1.15Мб |
| raw_group_agg.png |
253.27Кб |
| README.pdf |
1.27Мб |
| Read this first.docx |
19.09Кб |
| Read this first.docx |
19.23Кб |
| rollingwindow3.png |
66.34Кб |
| rows_cols.png |
52.10Кб |
| rows_cols2.png |
12.83Кб |
| sample_data.csv |
260б |
| sample_data2.csv |
63б |
| sample_df.png |
49.52Кб |
| sample_missing.csv |
225б |
| Scrape Diamonds.ipynb |
7.47Кб |
| series_components.png |
58.55Кб |
| sf_employee_compensation.csv |
4.55Мб |
| simple_figure_tight.png |
19.29Кб |
| simple_figure.png |
13.09Кб |
| Solutions.ipynb |
41.98Кб |
| Solutions.ipynb |
345.16Кб |
| Solutions.ipynb |
154.51Кб |
| Solutions.ipynb |
128.39Кб |
| Solutions.ipynb |
58.16Кб |
| Solutions.ipynb |
376.21Кб |
| Solutions.ipynb |
1.12Мб |
| Solutions.ipynb |
142.76Кб |
| Solutions.ipynb |
1.99Мб |
| Solutions.ipynb |
989б |
| Solutions.ipynb |
549.78Кб |
| split-apply-combine.png |
317.58Кб |
| stocks10.csv |
311.47Кб |
| stocks3.csv |
84.71Кб |
| store_transactions.csv |
779б |
| temp_flow_pressure.csv |
234б |
| thread_needle.png |
20.47Кб |
| titanic_data_dictionary.csv |
385б |
| titanic.csv |
59.76Кб |
| triangles.csv |
954б |
| weather.csv |
94.33Кб |
| weight_loss.csv |
147б |