numpy - 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][1]:

usecolsint, 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.