Python pandas printing correct dataframe-CodePudding

I am reading from a csv-file, and have the current values in my dataframe, where width and height is min and max value.

name     width       heigth
apple    [10, 20]    [15, 18]

And now i want to split and format the columns and print them:

pd.DataFrame(df['width'].str.split(',').tolist(), columns=['width-min', 'width-max'])
pd.DataFrame(df['height'].str.split(',').tolist(), columns=['height-min', 'height-max'])

print(tabulate(df, headers='keys', tablefmt='psql'))

My problem is that it stills print:

name     width       heigth
apple    [10, 20]    [15, 18]

Whereas I want it to print:

name     min-width   max-width   min-height   max-height
apple    10          20          15           18

What am I doing wrong?

CodePudding user response：

This code can help you ,

a = {"name":['apple'],
     'width': ['[10, 20]'],
     'heigth': ['[15, 18]']}

import pandas as pd
df = pd.DataFrame(a)

df['min-width'], df['max-width'] = df['width'].apply(lambda x: x.strip('][').split(', ')[0]), \
                                   df['width'].apply(lambda x: x.strip('][').split(', ')[1])

df['min-height'], df['max-height'] = df['heigth'].apply(lambda x: x.strip('][').split(', ')[0]), \
                                   df['heigth'].apply(lambda x: x.strip('][').split(', ')[1])

df = df.drop(['width','heigth'],axis=1)

The df will be,

    name min-width max-width min-height max-height
0  apple        10        20         15         18

please let me know if that helps you !! Also, I am open to optimized suggestions!!

CodePudding user response：

You do not save the resulting df. You need to assign the modified df to a new variable or overwrite the original.

EDIT:

#create a separate df for each split operation (height and width separately)

df_1 = pd.DataFrame(df['width'].str.split(',').tolist(), columns=['width-min', 'width-max'])
df_2 = pd.DataFrame(df['height'].str.split(',').tolist(), columns=['height-min', 'height-max'])

# join the two dataframes on a common index or column
pd.merge(df_1, df_2, on=["enter common column here"])