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In Python pandas, get the column index from the column name.


When retrieving a column index in R, you could do it by using the name of the column.

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

Is it possible to get the same result with pandas dataframes?

Asked by ak3nat0n

Solution #1

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 require this on a regular basis. Access by name usually works for me (df[“pear”], df[[“apple”, “orange”]], or maybe df.columns.isin([“orange”, “pear”]), but I can see why you’d prefer the index number in some circumstances.

Answered by DSM

Solution #2

Here’s how to solve it using list comprehension. cols is a list of columns for which an index should be created:

[df.columns.get_loc(c) for c in cols if c in df]

Answered by snovik

Solution #3

DSM’s solution works, but you could do (df.columns == name) if you needed a direct equivalent. nonzero()

Answered by Wes McKinney

Solution #4

When you need to locate multiple column matches, you can use a vectorized solution with the searchsorted technique. An implementation would be – with df as the dataframe and query cols as the column names to be searched for.

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
   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])

Answered by Divakar

Solution #5

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]


Other ways:


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

Answered by salhin

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