I have the below data frame.
df3=pd.DataFrame(columns=["Devices","months"])
I am getting row value from a loop row, print(data)
Devices months
1 Powerbank Feb month
When I am adding this data row to my df3 I am getting an error.
df3.loc[len(df3)]=data
ValueError: cannot set a row with mismatched columns
use
df3 = pd.concat([df3, data], axis=0)
or as suggested by @Wen use
df3 = df3.append(data)
From https://pandas.pydata.org/pandas-docs/stable/merging.html:
It is worth noting however, that concat (and therefore append) makes a full copy of the data, and that constantly reusing this function can create a significant performance hit. If you need to use the operation over several datasets, use a list comprehension.
You should use loc, like you were trying to do, and with a dictionary where the keys are the column names and the values are the data of the row being added.
import pandas as pd
df3 = pd.DataFrame(columns=["Devices","months"])
new_entry = {'Devices': 'device1', 'months': 'month1'}
df3.loc[len(df3)] = new_entry
If someone is looking to append new row which is in dictionary format, below will help.
In [6]: df Out[6]: Devices months 0 Powerbank Feb month In [7]:
In [7]: dictionary_row = {"Devices":"Laptop","months":"Mar month"} In [8]: df = df.append(dictionary_row, ignore_index=True) In [9]: df Out[9]: Devices months 0 Powerbank Feb month 1 Laptop Mar month In [10]:
Hope that helps.
As the error suggests the number of columns should of the data being inserted into the dataframe must match the number of columns of the dataframe
>>> df3=pd.DataFrame(columns=["Devices","months"])
>>> df3.loc[len(df3)] = ['Powerbank','Feb']
>>> df3
Devices months
0 Powerbank Feb
>>> data = ['powerbank','feb']
>>> df3.loc[len(df3)] = data
>>> df3
Devices months
0 Powerbank Feb
1 powerbank feb
append