Replacing missing values using Pandas in Python

Dataset is a collection of attributes and rows. Data set can have missing data that are represented by NA in Python and in this article, we are going to replace missing values in this article

We consider this data set: Dataset

data set

In our data contains missing values in quantity, price, bought, forenoon and afternoon columns,

So, We can replace missing values in the quantity column with mean, price column with a median, Bought column with standard deviation. Forenoon column with the minimum value in that column. Afternoon column with maximum value in that column.

Approach:

• Import the module
• Load data set
• Fill in the missing values
• Verify data set

Syntax:

Mean: data=data.fillna(data.mean())

Median: data=data.fillna(data.median())

Standard Deviation: data=data.fillna(data.std())

Min: data=data.fillna(data.min())

Max: data=data.fillna(data.max())

Below is the Implementation:

Python3

# importing pandas module
import pandas as pd

# loading data set
data = pd.read_csv('item.csv')

# display the data
print(data)

Output:

Then after we will proceed with Replacing missing values with mean, median, mode, standard deviation, min & max

Python3

# replacing missing values in quantity
# column with mean of that column
data['quantity'] = data['quantity'].fillna(data['quantity'].mean())

# replacing missing values in price column
# with median of that column
data['price'] = data['price'].fillna(data['price'].median())

# replacing missing values in bought column with
# standard deviation of that column
data['bought'] = data['bought'].fillna(data['bought'].std())

# replacing missing values in forenoon  column with
# minimum number of that column
data['forenoon'] = data['forenoon'].fillna(data['forenoon'].min())

# replacing missing values in afternoon  column with
# maximum number of that column
data['afternoon'] = data['afternoon'].fillna(data['afternoon'].max())

print(Data)

Output: