My top 25 pandas tricks

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AI Summary

Summary of Top 25 pandas Tricks Video

1. Show Installed Versions
   • Check pandas version with pd.__version__.
   • Use show_versions() to see versions of dependencies.
2. Create an Example DataFrame
   • Use a dictionary to construct a DataFrame.
   • For larger DataFrames, utilize np.random.rand() and coerce a string of letters to a list for non-numeric column names.
3. Rename Columns
   • Use rename() method with a dictionary for flexible renaming.
   • Overwrite columns attribute to rename all columns.
   • Use str.replace() to replace characters in column names.
   • Add prefix or suffix with add_prefix() or add_suffix().
4. Reverse Row Order
   • Reverse rows with loc[::-1].
   • Reset index after reversing with reset_index(drop=True).
5. Reverse Column Order
   • Reverse columns with loc[:, ::-1].
6. Select Columns by Data Type
   • Use select_dtypes() to filter columns by data type.
7. Convert Strings to Numbers
   • Convert data types with astype().
   • Handle invalid input with pd.to_numeric() and errors='coerce'.
8. Reduce DataFrame Size
   • Use usecols parameter to read only needed columns.
   • Convert object columns with categorical data to category data type.
9. Build a DataFrame from Multiple Files (Row-wise)
   • Use glob module to concatenate multiple files into one DataFrame row-wise.
10. Build a DataFrame from Multiple Files (Column-wise)
    • Use glob and concat to combine files column-wise.
11. Create a DataFrame from the Clipboard
    • Use read_clipboard() to read data copied to the clipboard.
12. Split a DataFrame into Two Random Subsets
    • Use sample() and drop() to split a DataFrame.
13. Filter a DataFrame by Multiple Categories
    • Use isin() to filter by multiple categories.
14. Filter a DataFrame by Largest Categories
    • Use value_counts() and nlargest() to filter by largest categories.
15. Handle Missing Values
    • Use isna() and dropna() to handle missing data.
16. Split a String into Multiple Columns
    • Use str.split() and expand=True to split strings into separate columns.
17. Expand a Series of Lists into a DataFrame
    • Use apply() with pd.Series to expand lists into a DataFrame.
18. Aggregate by Multiple Functions
    • Use groupby() and agg() to aggregate by multiple functions.
19. Combine the Output of an Aggregation with a DataFrame
    • Use transform() to add an aggregation result as a new column.
20. Select a Slice of Rows and Columns
    • Use loc to select a subset of rows and columns.
21. Reshape a MultiIndexed Series
    • Use unstack() to convert a MultiIndexed Series into a DataFrame.
22. Create a Pivot Table
    • Use pivot_table() to create pivot tables and add totals with margins=True.
23. Convert Continuous Data into Categorical Data
    • Use cut() to bin continuous data into categories.
24. Change Display Options
    • Use set_option() to change display precision.
25. Style a DataFrame
    • Use style.format() and chaining methods to style DataFrames.

Bonus Trick: Profile a DataFrame

• Use pandas-profiling to generate an interactive HTML report of a DataFrame.