Get mapping of categorical variables in pandas

by

Method 1

You can create a dictionary mapping by enumerating (similar to creating a dictionary from a list by creating dictionary keys from the list indices):

dict( enumerate(df['x'].cat.categories ) )

# {0: 'bad', 1: 'good', 2: 'great'}

Method 2

Alternatively, you could map the values and codes in every row:

dict( zip( df['x'].cat.codes, df['x'] ) )

# {0: 'bad', 1: 'good', 2: 'great'}

It’s a little more transparent what is happening here, and arguably safer for that reason. It is also much less efficient as the length of the arguments to zip() is len(df) whereas the length of df['x'].cat.categories is only the count of unique values and generally much shorter than len(df).

Additional Discussion

The reason Method 1 works is that the categories have type Index:

type( df['x'].cat.categories )

# pandas.core.indexes.base.Index

and in this case you look up values in an index just as you would a list.

There are a couple of ways to verify that Method 1 works. First, you can just check that a round trip retains the correct values:

(df['x'] == df['x'].cat.codes.map( dict( 
            enumerate(df['x'].cat.categories) ) ).astype('category')).all()
# True

or you can check that Method 1 and Method 2 give the same answer:

(dict( enumerate(df['x'].cat.categories ) ) == dict( zip( df['x'].cat.codes, df['x'] ) ))

# True

More Related Contents:

  1. change column name in pandas, which will be used to change name for 1 column
  2. How to save a new sheet in an existing excel file, using Pandas?
  3. Pandas read in table without headers
  4. Constructing pandas DataFrame from values in variables gives “ValueError: If using all scalar values, you must pass an index”
  5. Can pandas automatically read dates from a CSV file?
  6. Pandas cannot open an Excel (.xlsx) file
  7. pandas dataframe str.contains() AND operation
  8. Python Pandas: Check if string in one column is contained in string of another column in the same row
  9. First non-null value per row from a list of Pandas columns
  10. Pandas: Filtering multiple conditions
  11. How to determine the length of lists in a pandas dataframe column
  12. Pandas fill missing values in dataframe from another dataframe
  13. Filter a pandas dataframe using values from a dict
  14. What is the difference between a pandas Series and a single-column DataFrame?
  15. Get data from pandas into a SQL server with PYODBC
  16. Find the unique values in a column and then sort them
  17. how do I calculate a rolling idxmax
  18. binning a dataframe in pandas in Python [duplicate]
  19. What is the difference between pandas agg and apply function?
  20. Pandas: Return Hour from Datetime Column Directly
  21. How to filter a pandas dataframe based on the length of a entry
  22. How can I group a stacked bar chart?
  23. How is pandas groupby method actually working?
  24. How to rotate x-axis tick labels in a pandas plot
  25. Pandas: Creating aggregated column in DataFrame
  26. How to use sklearn fit_transform with pandas and return dataframe instead of numpy array?
  27. pandas convert from datetime to integer timestamp
  28. geopandas point in polygon
  29. Rename columns in Pandas based on a dictionary
  30. When is it appropriate to use df.value_counts() vs df.groupby(‘…’).count()?

Leave a Comment