Select dataframe columns that starts with certain string and additional columns

Question

I have a dataframe with columns: 'Id', 'Category', 'Shop', ....., 'Brandtxsu1', 'Brandxyw2', ...

I want to select columns: ID, Category, and start with Brand. I can select the columns that start with Brand using the following code, but how do I select ID and Category?

df[df.columns[pd.Series(df.columns).str.startswith('Brand')]]

Asked By: M J

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Source

Answer 1

You can provide a list of columns you want to filter for:

cols = [c for c in df.columns if c.startswith('Brand') or c in ('Id', 'Category', ...)]
df[cols]

Answered By: Andreas

Answer 2

You can try join with filter

out = df[['ID', 'Category']].join(df.filter(regex='^Brand'))

Answered By: Ynjxsjmh

Answer 3

Solution

Select the ‘Id’ and ‘Category’ columns from the dataframe.
Select the columns from the dataframe whose column names start with ‘Brand’.
Join them together.

Sample Code

import pandas as pd

df = pd.DataFrame({
    'Id':['001', '002', '003', '004'], 
    'Category':['A', 'A', 'S', 'B'],
    'Shop':['Shop1', 'Shop2', 'Shop3', 'Shop4'],
    'Brandtxsu1':[1, 1, 1, 1],
    'Brandxyw2':[2, 2, 2, 2]
})

df_output = df[['Id', 'Category']].join(df.loc[:, df.columns.str.startswith('Brand')])

print(df_output)

Output

    Id Category  Brandtxsu1  Brandxyw2
0  001        A           1          2
1  002        A           1          2
2  003        S           1          2
3  004        B           1          2

Answered By: Brian.Z

Answer 4

One option is with pd.filter:

df.filter(regex="Id|Category|Brand.+")
Out[23]: 
    Id Category  Brandtxsu1  Brandxyw2
0  001        A           1          2
1  002        A           1          2
2  003        S           1          2
3  004        B           1          2

Another option is with pyjanitor select_columns:

# pip install pyjanitor
import pandas as pd
import janitor
df.select_columns('Id', 'Category', 'Brand*')
    Id Category  Brandtxsu1  Brandxyw2
0  001        A           1          2
1  002        A           1          2
2  003        S           1          2
3  004        B           1          2

Answered By: sammywemmy

Select dataframe columns that starts with certain string and additional columns

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