Sometimes the dataframe contains an empty column and may pose a real problem in the real life scenario. Missing Data can also refer to as NA(Not Available) values in pandas. In DataFrame sometimes many datasets simply arrive with missing data, either because it exists and was not collected or it never existed. In this article, let’s see how to fill empty columns in dataframe using pandas.
Note: Link of csv file here.
Fill empty column:
Python3
import pandas as pd df = pd.read_csv("Persons.csv") df |
First, we import pandas after that we load our CSV file in the df variable. Just try to run this in jupyter notebook or colab.
Output:
Python3
df.set_index('Name ', inplace=True) df |
This line used to remove index value, we don’t want that, so we remove it.
Output:
There are several methods used to fill the empty columns.we going to saw it one by one
Method 1:
In this method, we will use “df.fillna(0)” which replace all NaN elements with 0s.
Example:
Python3
df1 = df.fillna(0) df1 |
Output:
Method 2:
In this method, we will use “df.fillna(method=’ffill’)” , which is used to propagate non-null values forward or backward.
Syntax: DataFrame.fillna(value=None, method=None, axis=None, inplace=False, limit=None, downcast=None)
Python3
df2 = df.fillna(method='ffill') df2 |
Output:
Method 3:
In this method we will use “df.interpolate()”
Syntax: DataFrame.interpolate(method=’linear’, axis=0, limit=None, inplace=False, limit_direction=None, limit_area=None, downcast=None, **kwargs)
Python3
df3 = df.interpolate() df3 |
Output:
