From the course: Data Analysis with Python and Pandas
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Jupyter installation and launch
From the course: Data Analysis with Python and Pandas
Jupyter installation and launch
“
- [Instructor] Okay, so in order to work with Jupyter Notebooks, we first need to install Anaconda. We're going to walk through the installation steps for Mac, and then we'll walk through the installation steps for PC. Although I will say that the instruction steps are extremely similar. The first thing we need to do is go to anaconda.com/products/distribution, and click Download. You should see the Apple icon next to the Download button. If you don't, just go ahead and click the additional installers below. Click X on the Anaconda Nucleus popup. There's no need to launch this. Launch the downloaded Anaconda.pkg file, and follow the installation steps. The default settings are going to be okay here. For PC, it's very similar. We should see the Windows icon next to the Download button. And if you don't, just click the Windows button down below. Once again, we're going to click X on the Anaconda Nucleus popup. Launch the downloaded Anaconda.exe file, and follow the installation steps…
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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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