CODEWITHSUNDEEP
pythonpandasdataframe
Meruemu
Meruemu

Reputation: 611

How can I select discrete columns from data frame

My data table looks like this. enter image description here With data.columns of following 60 columns

['WeatherHR0', 'WeatherHR1', 'WeatherHR2', 'WeatherHR3', 'WeatherHR4',
       'WeatherHR5', 'WeatherHR6', 'WeatherHR7', 'WeatherHR8', 'WeatherHR9',
       'WeatherHR10', 'WeatherHR11', 'WeatherHR12', 'WeatherHR13',
       'WeatherHR14', 'WeatherHR15', 'WeatherHR16', 'WeatherHR17',
       'WeatherHR18', 'WeatherHR19', 'WeatherHR20', 'WeatherHR21',
       'WeatherHR22', 'WeatherHR23', 'AvgDB', 'HDD0', 'HDD5', 'HDD10', 'HDD13',
       'HDD18', 'CDD15', 'CDD18', 'Peak Average', 'Day of Week', 'Holiday',
       'HR1', 'HR2', 'HR3', 'HR4', 'HR5', 'HR6', 'HR7', 'HR8', 'HR9', 'HR10',
       'HR11', 'HR12', 'HR13', 'HR14', 'HR15', 'HR16', 'HR17', 'HR18', 'HR19',
       'HR20', 'HR21', 'HR22', 'HR23', 'HR24', 'Max']

At times, I wanted to select data from multiple columns that are separated by unwanted columns (unwanted as for that moment, might needed for later).

I wanted to do something like df.loc[['WeatherHR0':WeatherHR23'+ 'Peak Average'+ 'HR0':'HR24']].

For example, I may want to select columns from WeatherHR0~WeatherHR23 + Peak Average + HR0~24 while keep other columns undropped.

I know I can drop the unselected columns/create new dataframe, but is there a pythonic way to selected discreted columns in pandas?

Upvotes: 1

Views: 715

Answers (1)

jfaccioni
jfaccioni

Reputation: 7519

you can create a list of strings (pythonically) and then use that to select the columns:

columns_of_interest = ([f'WeatherHR{i}' for i in range(24)] + 
                       ['Peak Average'] + 
                       [f'HR{i}' for i in range(25)])
data[columns_of_interest]

Of course, this strategy works best if there are common patterns in the column names.

Upvotes: 2

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