From the course: Data Analysis with Python and Pandas
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Exploring DataFrames: Info and describe
From the course: Data Analysis with Python and Pandas
Exploring DataFrames: Info and describe
- [Instructor] All right, so now let's shift gears from some DataFrame methods that allow us to quickly take a look at the top, bottom, and random rows in our DataFrame. Those methods are great for doing a quick spot check to make sure that all the rows that we wanted to read in are read in and there weren't any major errors upon reading in our DataFrame. The .info() method returns information on the key DataFrame properties, as well as things like memory usage and missing values in our DataFrame. So let's go ahead and call the .info() method on our retail DataFrame. In the top piece ,we get information on our axis. We get information on both our row index, as well as our column index. So we can think about this as combining the information we would get from shape and axis. The next piece is the Column position. So our first column has index zero, our last column is on promotion, and we get the column titles, as well as the data types of each column. And down below that, we get a…
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Contents
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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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