I have this:
df = pd.DataFrame({'my_col' : ['red', 'red', 'green']})
my_col
red
red
green
I want this: df2 = pd.DataFrame({'red' : [True, True, False], 'green' : [False, False, True]})
red green
True False
True False
False True
Is there an elegant way to do this?
You can do this:
for color in df['my_col'].unique():
df[color] = df['my_col'] == color
df2 = df[df['my_col'].unique()]
It will loop over each color in my_col and adds a column to df with the name of the color and True/False whether it is equal to the color. Finally extract df2 from df by selecting only the color columns.
Another option is to start with an empty dataframe for df2 and immediately add the columns to this dataframe:
df2 = pd.DataFrame()
for color in df['my_col'].unique():
df2[color] = df['my_col'] == color
Output:
red green
0 True False
1 True False
2 False True
Python functionality get_dummies can work for this.
import pandas as pd
import numpy as np
df = pd.DataFrame({'my_col': ['red', 'red', 'green']})
new_df = pd.get_dummies(df, dtype=bool)
new_df[:] = np.where(pd.get_dummies(df, dtype=bool), 'True', 'False')
new_df.rename(columns={'my_col_green': 'green', 'my_col_red': 'red'}, inplace=True)
print(new_df)
Considering that the original dataframe is df, one can use:
Option 1
Using pandas.get_dummies, one can do the following
df2 = pd.get_dummies(df['my_col'], dtype=bool)
[Out]:
green red
0 False True
1 False True
2 True False
If one wants the column red to appear first, a one-liner would look like the following
df2 = pd.get_dummies(df['my_col'], dtype=bool)[['red', 'green']]
[Out]:
red green
0 True False
1 True False
2 False True
Option 2
Using pandas.Series.str.get_dummies, one can do the following
df2 = df['my_col'].str.get_dummies().astype(bool)
[Out]:
green red
0 False True
1 False True
2 True False
If one wants the column red to appear first, a one-liner would look like the following
df2 = df['my_col'].str.get_dummies().astype(bool)[['red', 'green']]
[Out]:
red green
0 True False
1 True False
2 False True
# reset index, to keep the rows count
df=df.reset_index()
# create a cross tab (don't miss negation for the resultset)
~(pd.crosstab(index=[df['index'],df['my_col']],
columns=df['my_col'])
.reset_index() # cleanup to match the output
.drop(columns=['index','my_col']) # drop unwanted columns
.rename_axis(columns=None) # remove axis name
.astype(bool)) # make it boolean
green red
0 True False
1 True False
2 False True
