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Pandas: Fill missing values in time series data

Pandas: DataFrame Exercise-74 with Solution

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

Python-Pandas Code Editor:

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Python: Tips of the Day

Inserting if statements using conditional list comprehensions:

x = [1, 2, 3, 4, 5, 6]
result = []
for idx in range(len(x)):
    if x[idx] % 2 == 0:
        result.append(x[idx] * 2)
    else:
        result.append(x[idx])
result

Output:

[1, 4, 3, 8, 5, 12]

[(element * 2 if element % 2 == 0 else element) for element in x]

Output:

[1, 4, 3, 8, 5, 12]

[element * 2 for element in x if element % 2 == 0]

Output:

[4, 8, 12]