how to read certain columns from Excel using Pandas - Python

Question

I am reading from an Excel sheet and I want to read certain columns: column 0 because it is the row-index, and columns 22:37. Now here is what I do:

import pandas as pd import numpy as np file_loc = "path.xlsx" df = pd.read_excel(file_loc, index_col=None, na_values=['NA'], parse_cols = 37) df= pd.concat([df[df.columns[0]], df[df.columns[22:]]], axis=1) 

But I would hope there is better way to do that! I know if I do parse_cols=[0, 22,..,37] I can do it, but for large datasets this doesn't make sense.

I also did this:

s = pd.Series(0) s[1]=22 for i in range(2,14):     s[i]=s[i-1]+1 df = pd.read_excel(file_loc, index_col=None, na_values=['NA'], parse_cols = s) 

But it reads the first 15 columns which is the length of s.

Answer 1

You can use column indices (letters) like this:

import pandas as pd import numpy as np file_loc = "path.xlsx" df = pd.read_excel(file_loc, index_col=None, na_values=['NA'], usecols="A,C:AA") print(df) 

Corresponding documentation:

usecols : int, str, list-like, or callable default None

• If None, then parse all columns.

• If str, then indicates comma separated list of Excel column letters and column ranges (e.g. “A:E” or “A,C,E:F”). Ranges are inclusive of both sides.

• If list of int, then indicates list of column numbers to be parsed.

• If list of string, then indicates list of column names to be parsed.

  New in version 0.24.0.

• If callable, then evaluate each column name against it and parse the column if the callable returns True.

Returns a subset of the columns according to behavior above.

New in version 0.24.0.