From the course: Data Analysis with Python and Pandas
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Solution: Mid-course project
From the course: Data Analysis with Python and Pandas
Solution: Mid-course project
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Contents
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pandas and NumPy intro2m 53s
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NumPy arrays and array properties7m 41s
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Challenge: Array basics1m 47s
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Solution: Array basics2m 2s
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Array creation8m 13s
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Random number generation5m 58s
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Challenge: Array creation1m 30s
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Solution: Array creation4m 22s
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Indexing and slicing arrays9m 9s
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Challenge: Indexing and slicing arrays1m 6s
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Solution: Indexing and slicing arrays2m 23s
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Array operations7m 45s
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Challenge: Array operations2m 6s
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Solution: Array operations4m 16s
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Filtering arrays and modifying array values10m 56s
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The where() function4m
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Challenge: Filtering and modifying arrays1m 57s
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Solution: Filtering and modifying arrays3m 11s
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Array aggregation6m 51s
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Array functions7m 41s
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Sorting arrays3m 51s
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Challenge: Aggregation and sorting1m 11s
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Solution: Aggregation and sorting1m 35s
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Vectorization4m 19s
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Broadcasting7m 8s
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Challenge: Bringing it all together2m 45s
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Solution: Bringing it all together6m 18s
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Key takeaways1m 56s
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Series basics10m
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pandas data types and type conversion6m 46s
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Challenge: Data types and type conversion2m 23s
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Solution: Data types and type conversion3m 5s
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The series index and custom indices7m 6s
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The .iloc accessor4m 33s
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The .loc accessor7m 3s
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Duplicate index values and resetting the index6m 33s
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Challenge: Accessing data and resetting the index2m 1s
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Solution: Accessing data and resetting the index2m 39s
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Filtering series and logical tests8m 19s
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Sorting series3m 45s
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Challenge: Sorting and filtering series57s
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Solution: Sorting and filtering series3m 24s
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Numeric series operations6m 31s
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Text series operations7m 4s
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Challenge: Series operations1m 36s
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Solution: Series operations3m 53s
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Numerical series aggregation5m 43s
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Categorical series aggregation3m 32s
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Challenge: Series aggregation50s
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Solution: Series aggregation4m 20s
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Missing data representation in pandas4m 29s
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Identifying missing data2m 15s
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Fixing missing data9m 27s
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Challenge: Missing data45s
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Solution: Missing data1m 35s
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Applying custom functions to series4m 6s
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pandas where() vs. NumPy where()6m 3s
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Challenge: apply() and where()1m 9s
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Solution: apply() and where()4m 37s
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Key takeaways1m 24s
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DataFrame basics4m 20s
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Creating a DataFrame4m 59s
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Challenge: DataFrame basics53s
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Solution: DataFrame basics1m 46s
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Exploring DataFrames: Heads, tails, and sample3m 35s
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Exploring DataFrames: Info and describe8m 20s
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Challenge: Exploring a DataFrame3m 12s
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Solution: Exploring a DataFrame4m 3s
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Accessing DataFrame columns4m 53s
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Accessing DataFrame data with .iloc and .loc6m 6s
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Challenge: Accessing DataFrame data1m 18s
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Solution: Accessing DataFrame data3m 23s
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Dropping columns and rows5m 54s
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Identifying and dropping duplicates7m
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Challenge: Dropping data1m 1s
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Solution: Dropping data2m 38s
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Missing data3m 17s
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Challenge: Missing data51s
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Solution: Missing data2m 13s
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Filtering DataFrames4m 29s
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Pro tip: The query() method4m 15s
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Challenge: Filtering DataFrames1m 29s
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Solution: Filtering DataFrames6m 46s
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Sorting DataFrames6m 53s
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Challenge: Sorting DataFrames44s
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Solution: Sorting DataFrames2m 45s
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Renaming and reordering columns3m 10s
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Challenge: Renaming and reordering columns54s
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Solution: Renaming and reordering columns3m 18s
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Arithmetic and Boolean column creation6m 22s
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Challenge: Arithmetic and Boolean columns1m 40s
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Solution: Arithmetic and Boolean columns3m 58s
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Pro tip: Advanced conditional columns with select()5m 59s
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Challenge: The select() function1m 46s
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Solution: The select() function3m 34s
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The map() method4m 24s
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Pro tip: Multiple column creation with assign()8m 19s
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Challenge: map() and assign()1m 24s
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Solution: map() and assign()2m 38s
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The categorical data type5m 31s
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Type conversion1m 37s
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Pro tip: Memory usage and data types6m 2s
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Pro tip: Downcasting numeric data types4m 58s
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Challenge: DataFrame data types1m 24s
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Solution: DataFrame data types3m 19s
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Key takeaways1m 33s
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Basic aggregations4m 14s
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The groupby() method4m 32s
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Challenge: groupby()1m 18s
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Solution: groupby()2m 11s
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Grouping by multiple columns4m 41s
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Challenge: Grouping by multiple columns1m 9s
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Solution: Grouping by multiple columns3m
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MultiIndex DataFrames7m 39s
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Modifying a MultiIndex4m 25s
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Challenge: MultiIndex DataFrames1m 17s
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Solution: MultiIndex DataFrames4m 1s
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The agg() method and named aggregations7m 22s
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Challenge: The agg() method1m 22s
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Solution: The agg() method3m 1s
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Pro tip: Transforming DataFrames6m 50s
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Challenge: Transforming a DataFrame1m 18s
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Solution: Transforming a DataFrame4m 27s
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Pivot tables in pandas6m 40s
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Multiple aggregation pivot tables2m 54s
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Pro tip: Pivot table heatmaps4m 35s
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Melting DataFrames6m 26s
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Challenge: pivot() and melt()1m 4s
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Solution: pivot() and melt()5m 39s
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Key takeaways1m 53s
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The Matplotlib API and the plot() method9m 33s
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Challenge: Basic line chart49s
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Solution: Basic line chart3m 1s
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Chart titles3m 26s
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Chart colors5m 13s
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Line styles2m 1s
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Chart legends and gridlines3m 51s
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Chart styles4m 8s
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Challenge: Stylized line chart1m 11s
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Solution: Stylized line chart1m 21s
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Subplots and figure size5m 28s
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Challenge: Subplots1m 33s
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Solution: Subplots2m 59s
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Bar charts6m 13s
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Grouped and stacked bar charts5m 9s
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Challenge: Bar charts1m 11s
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Solution: Bar charts2m 19s
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Pie charts and scatterplots6m 52s
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Challenge: Scatterplots1m
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Solution: Scatterplots2m 10s
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Histograms3m 46s
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Challenge: Histograms33s
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Solution: Histograms1m 18s
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Saving plots and further exploration3m 41s
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Key takeaways2m 12s
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Times in Python and pandas3m 8s
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Converting to datetimes6m 16s
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Formatting dates5m 20s
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Date and time parts3m 4s
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Challenge: pandas datetime basics1m 23s
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Solution: pandas datetime basics2m 10s
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Time deltas and arithmetic6m 54s
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Challenge: Time deltas1m 10s
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Solution: Time deltas1m 29s
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Time series indices3m 58s
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Missing time series data4m 45s
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Challenge: Missing time series data1m 44s
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Solution: Missing time series data2m 13s
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Shifting time series3m 16s
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Pro tip: diff()2m 54s
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Challenge: shift() and diff()1m 39s
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Solution: shift() and diff()2m 47s
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Aggregation and resampling4m 6s
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Challenge: Resampling41s
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Solution: Resampling1m 53s
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Rolling aggregations4m 35s
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Challenge: Rolling aggregations45s
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Solution: Rolling aggregations55s
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Key takeaways1m 37s
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Preprocessing with read_csv()5m 30s
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Column selection3m 46s
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Row selection and missing values4m 44s
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Parsing dates and data types3m 34s
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Pro tip: Converters2m 48s
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Challenge: Importing data1m 44s
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Solution: Importing data4m 19s
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Importing from text and Excel files5m 21s
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Exporting to flat files2m 9s
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Challenge: Importing and exporting Excel data1m 19s
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Solution: Importing and exporting Excel data1m 54s
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Working with SQL databases6m 22s
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Other supported file formats2m 34s
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Key takeaways1m 11s
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Why use multiple tables?1m 56s
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Appending DataFrames4m 41s
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Challenge: Appending DataFrames1m 25s
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Solution: Appending DataFrames1m 38s
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Joining DataFrames2m 22s
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Join types3m 42s
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Inner joins3m 22s
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Left joins4m 22s
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Challenge: Joining DataFrames1m 55s
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Solution: Joining DataFrames5m 41s
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The join() method1m 56s
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Key takeaways1m 16s
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