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Handling Missing Data (isna, fillna, dropna)

Why missing data is normal

Missing values happen because of:

  • Optional form fields (e.g., phone number)
  • Data entry errors
  • Failed joins/merges
  • Incomplete logs

Pandas typically represents missing values as NaNNaN (and sometimes NoneNone).

Setup example with missing values

Missing values example
import pandas as pd
import numpy as np
 
df = pd.DataFrame({
    "name": ["Asha", "Ravi", "Meera", None],
    "age": [23, np.nan, 26, 31],
    "city": ["Pune", "Delhi", None, "Pune"],
    "score": [88, 91, np.nan, 95],
})
 
print(df)
Missing values example
import pandas as pd
import numpy as np
 
df = pd.DataFrame({
    "name": ["Asha", "Ravi", "Meera", None],
    "age": [23, np.nan, 26, 31],
    "city": ["Pune", "Delhi", None, "Pune"],
    "score": [88, 91, np.nan, 95],
})
 
print(df)

Detect missing values

isna()isna() / isnull()isnull()

isna
print(df.isna())
isna
print(df.isna())

Count missing per column

Missing counts
print(df.isna().sum())
Missing counts
print(df.isna().sum())

Remove missing values: dropna()dropna()

Drop rows with any missing values

Drop rows with any NA
clean = df.dropna()
print(clean)
Drop rows with any NA
clean = df.dropna()
print(clean)

Drop rows where a specific column is missing

Drop rows where score is missing
clean = df.dropna(subset=["score"])
print(clean)
Drop rows where score is missing
clean = df.dropna(subset=["score"])
print(clean)

Fill missing values: fillna()fillna()

Fill with a constant

Fill missing city
filled = df.copy()
filled["city"] = filled["city"].fillna("Unknown")
print(filled)
Fill missing city
filled = df.copy()
filled["city"] = filled["city"].fillna("Unknown")
print(filled)

Fill numeric missing values with mean/median

Fill numeric with median
filled = df.copy()
filled["score"] = filled["score"].fillna(filled["score"].median())
print(filled)
Fill numeric with median
filled = df.copy()
filled["score"] = filled["score"].fillna(filled["score"].median())
print(filled)

Forward fill / backward fill

Useful for time series or repeated categories.

Forward fill
filled = df.copy()
filled["city"] = filled["city"].ffill()
print(filled)
Forward fill
filled = df.copy()
filled["city"] = filled["city"].ffill()
print(filled)

Important: be explicit

Before deciding how to handle missing data, ask:

  • Is missingness random or meaningful?
  • Should missing values be removed or imputed?
  • Will filling change the meaning of the data?

In analytics, documenting missing-data decisions is part of good practice.

Visualize it

diagram Two ways to handle missing values mermaid
After detecting missing values, you either drop the affected rows/columns or fill them in.

🧪 Try It Yourself

Exercise 1 – Detect Missing Values

Exercise 2 – Drop Rows With Any Missing Value

Exercise 3 – Fill Missing Values With the Mean

Next

With missing values under control, move on to Cleaning Data (astype, duplicates, string cleaning) to fix types, duplicates, and messy text.

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