Handling missing values in a dataset in Python.txt

Importing Libraries

To begin with handling missing values, you will need to import the necessary libraries. The most commonly used library for this purpose is pandas. You can import pandas using the following code:


import pandas as pd
Loading Data

Before you can start handling missing values, you will need to load the data into a pandas DataFrame. You can do this using the pandas read_csv function, or by loading data from another source such as a database.


# Load data from a CSV file
df = pd.read_csv('data.csv')
Identifying Missing Values

You can use the isnull method to identify cells with missing values in a DataFrame. This method returns a boolean mask indicating which cells contain missing values.


# Identify cells with missing values
df.isnull()

# Count the number of missing values in each column
df.isnull().sum()
Dropping Rows with Missing Values

You can drop rows with missing values using the dropna method.


# Drop rows with any missing values
df.dropna()

# Drop rows with all missing values
df.dropna(how='all')

# Drop rows with at least n non-missing values
df.dropna(thresh=n)
Filling Missing Values

You can fill missing values with a scalar value using the fillna method.


# Fill missing values with a scalar value
df.fillna(value)

# Fill missing values with a different value for each column
df.fillna({'col_1': value_1, 'col_2': value_2})

# Forward-fill missing values
df.ffill()

# Backward-fill missing values
df.bfill()