Is there a way to check if a column exists in a Pandas DataFrame?
Suppose that I have the following DataFrame:
>>> import pandas as pd
>>> from random import randint
>>> df = pd.DataFrame({'A': [randint(1, 9) for x in xrange(10)],
'B': [randint(1, 9)*10 for x in xrange(10)],
'C': [randint(1, 9)*100 for x in xrange(10)]})
>>> df
A B C
0 3 40 100
1 6 30 200
2 7 70 800
3 3 50 200
4 7 50 400
5 4 10 400
6 3 70 500
7 8 30 200
8 3 40 800
9 6 60 200
and I want to calculate df['sum'] = df['A'] + df['C']
But first I want to check if df['A'] exists, and if not, I want to calculate df['sum'] = df['B'] + df['C'] instead.
This will work:
if 'A' in df:
But for clarity, I'd probably write it as:
if 'A' in df.columns:
Just to suggest another way without using if statements, you can use the get() method for DataFrames. For performing the sum based on the question:
df['sum'] = df.get('A', df['B']) + df['C']
The DataFrame get method has similar behavior as python dictionaries.
To check if one or more columns all exist, you can use set.issubset, as in:
if set(['A','C']).issubset(df.columns):
df['sum'] = df['A'] + df['C']
As @brianpck points out in a comment, set([]) can alternatively be constructed with curly braces,
if {'A', 'C'}.issubset(df.columns):
See this question for a discussion of the curly-braces syntax.
Or, you can use a list comprehension, as in:
if all([item in df.columns for item in ['A','C']]):
Source: Stackoverflow.com