Fix ValueError: cannot set a row with mismatched columns?

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One error you may encounter when using pandas is:

ValueError: cannot set a row with mismatched columns

This error occurs when you attempt to add a new row to a pandas DataFrame but the number of values in the new row doesn’t match the number of columns in the existing DataFrame.

The following example shows how to fix this error in practice.

How to Reproduce the Error

Suppose we create the following pandas DataFrame:

import pandas as pd

#create DataFrame
df = pd.DataFrame({'team': ['A', 'B', 'C', 'D', 'E', 'F', 'G', 'H', 'I'],
                   'points': [18, 22, 19, 14, 14, 11, 20, 28, 22],
                   'assists': [5, 7, 7, 9, 12, 9, 9, 4, 8],
                   'rebounds': [11, 8, 10, 6, 6, 5, 9, 12, 9]})

#view DataFrame
df

	team	points	assists	rebounds
0	A	18	5	11
1	B	22	7	8
2	C	19	7	10
3	D	14	9	6
4	E	14	12	6
5	F	11	9	5
6	G	20	9	9
7	H	28	4	12
8	I	22	8	9

Now suppose we try to append a new row to the end of the DataFrame:

#define new row to append
new_team = ['J', 30]

#append row to DataFrame
df.loc[len(df)] = new_team

#view updated DataFrame
df

ValueError: cannot set a row with mismatched columns

We receive a ValueError because the new row we’re trying to append only contains two values, but the existing DataFrame has four columns.

How to Fix the Error

The easiest way to fix this error is to use the append() function to add the new row to the end of the DataFrame, which will automatically fill in missing values with NaN:

The following syntax shows how to use this function in practice:

#define new row to append
new = ['J', 30]

#append row to end of DataFrame
df = df.append(pd.Series(new, index=df.columns[:len(new)]), ignore_index=True)

#view updated DataFrame
df

	team	points	assists	rebounds
0	A	18	5.0	11.0
1	B	22	7.0	8.0
2	C	19	7.0	10.0
3	D	14	9.0	6.0
4	E	14	12.0	6.0
5	F	11	9.0	5.0
6	G	20	9.0	9.0
7	H	28	4.0	12.0
8	I	22	8.0	9.0
9	J	30	NaN	NaN

Notice that we don’t receive any ValueError and the new row has been appended to the end of the DataFrame.

Also notice that both of the missing values in the new row were simply filled in with NaN values.