Dealing with Missing Data#

Missing data can significantly impact the results of your data analysis if not properly handled. Pandas provides several methods to deal with missing values, allowing you to either fill these gaps or make interpolations based on the existing data. This chapter explores methods like interpolation, forward filling, and backward filling.

Interpolate Missing Values#

Interpolation is a method of estimating missing values by using other available data points. It is particularly useful in time series data where this can estimate the trends accurately:

import pandas as pd
import numpy as np

# Sample DataFrame with missing values
data = {'value': [1, np.nan, np.nan, 4, 5]}
df = pd.DataFrame(data)

# Interpolating missing values
df['value'] = df['value'].interpolate()
print(df)

Result:

   value
0    1.0
1    2.0
2    3.0
3    4.0
4    5.0

Here, interpolate() linearly estimates the missing values between the existing numbers.

Forward Fill Missing Values#

Forward fill (ffill) propagates the last observed non-null value forward until another non-null value is encountered:

# Sample DataFrame with missing values
data = {'value': [1, np.nan, np.nan, 4, 5]}
df = pd.DataFrame(data)

# Applying forward fill
df['value'].ffill(inplace = True)
print(df)

Result:

   value
0    1.0
1    1.0
2    1.0
3    4.0
4    5.0

Backward Fill Missing Values#

Backward fill (bfill) propagates the next observed non-null value backwards until another non-null value is met:

# Sample DataFrame with missing values
data = {'value': [1, np.nan, np.nan, 4, 5]}
df = pd.DataFrame(data)

# Applying backward fill
df['value'].bfill(inplace = True)
print(df)

Result:

   value
0    1.0
1    4.0
2    4.0
3    4.0
4    5.0

These methods provide you with flexible options for handling missing data based on the nature of your dataset and the specific requirements of your analysis. Correctly addressing missing data is crucial for maintaining the accuracy and reliability of your analytical results.