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Get column index from column name in python pandas


Question

In R when you need to retrieve a column index based on the name of the column you could do

idx <- which(names(my_data)==my_colum_name)

Is there a way to do the same with pandas dataframes?

2017/12/19
1
236
12/19/2017 2:56:13 AM

Accepted Answer

Sure, you can use .get_loc():

In [45]: df = DataFrame({"pear": [1,2,3], "apple": [2,3,4], "orange": [3,4,5]})

In [46]: df.columns
Out[46]: Index([apple, orange, pear], dtype=object)

In [47]: df.columns.get_loc("pear")
Out[47]: 2

although to be honest I don't often need this myself. Usually access by name does what I want it to (df["pear"], df[["apple", "orange"]], or maybe df.columns.isin(["orange", "pear"])), although I can definitely see cases where you'd want the index number.

2012/10/23
378
10/23/2012 12:06:36 AM

Here is a solution through list comprehension. cols is the list of columns to get index for:

[df.columns.get_loc(c) for c in cols if c in df]
2019/01/22

DSM's solution works, but if you wanted a direct equivalent to which you could do (df.columns == name).nonzero()

2012/10/23

When you might be looking to find multiple column matches, a vectorized solution using searchsorted method could be used. Thus, with df as the dataframe and query_cols as the column names to be searched for, an implementation would be -

def column_index(df, query_cols):
    cols = df.columns.values
    sidx = np.argsort(cols)
    return sidx[np.searchsorted(cols,query_cols,sorter=sidx)]

Sample run -

In [162]: df
Out[162]: 
   apple  banana  pear  orange  peach
0      8       3     4       4      2
1      4       4     3       0      1
2      1       2     6       8      1

In [163]: column_index(df, ['peach', 'banana', 'apple'])
Out[163]: array([4, 1, 0])
2017/09/04

In case you want the column name from the column location (the other way around to the OP question), you can use:

>>> df.columns.get_values()[location]

Using @DSM Example:

>>> df = DataFrame({"pear": [1,2,3], "apple": [2,3,4], "orange": [3,4,5]})

>>> df.columns

Index(['apple', 'orange', 'pear'], dtype='object')

>>> df.columns.get_values()[1]

'orange'

Other ways:

df.iloc[:,1].name

df.columns[location] #(thanks to @roobie-nuby for pointing that out in comments.) 
2019/12/24

how about this:

df = DataFrame({"pear": [1,2,3], "apple": [2,3,4], "orange": [3,4,5]})
out = np.argwhere(df.columns.isin(['apple', 'orange'])).ravel()
print(out)
[1 2]
2019/11/15