Pandas: Fill missing values in time series data

Last update on September 06 2025 12:48:49 (UTC/GMT +8 hours)

74. Fill Missing Values in Time Series Data

Write a Pandas program to fill missing values in time series data.

From Wikipedia , in the mathematical field of numerical analysis, interpolation is a type of estimation, a method of constructing new data points within the range of a discrete set of known data points.

Sample Solution :

Python Code :

import pandas as pd
import numpy as np
sdata = {"c1":[120, 130 ,140, 150, np.nan, 170], "c2":[7, np.nan, 10, np.nan, 5.5, 16.5]}
df = pd.DataFrame(sdata)
df.index = pd.util.testing.makeDateIndex()[0:6]
print("Original DataFrame:")
print(df)
print("\nDataFrame after interpolate:")
print(df.interpolate())

Sample Output:

Original DataFrame:
               c1    c2
2000-01-03  120.0   7.0
2000-01-04  130.0   NaN
2000-01-05  140.0  10.0
2000-01-06  150.0   NaN
2000-01-07    NaN   5.5
2000-01-10  170.0  16.5

DataFrame after interpolate:
               c1     c2
2000-01-03  120.0   7.00
2000-01-04  130.0   8.50
2000-01-05  140.0  10.00
2000-01-06  150.0   7.75
2000-01-07  160.0   5.50
2000-01-10  170.0  16.50

For more Practice: Solve these Related Problems:

Write a Pandas program to interpolate missing values in a time series DataFrame and then plot the resulting series.
Write a Pandas program to fill missing time series data using forward fill and then compare with backward fill results.
Write a Pandas program to apply linear interpolation to missing values in a time-indexed DataFrame and then compute the difference from original data.
Write a Pandas program to fill missing values in a time series by using a custom interpolation method based on moving averages.

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