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    Pandas Drop Rows with NaN Values in DataFrame

    Pandas Drop Rows with NaN Values in DataFrame

    To drop rows with NaN (null) values in a Pandas DataFrame, you can use the dropna() function. Python doesn't support Null hence any missing data is represented as None or NaN. NaN stands for Not A Number and serves as one of the typical methods for indicating missing values within datasets. None/NaN values are one of the major problems in Data Analysis hence before we process either you need to drop rows that have NaN values or replace NaN with empty for Strings and replace NaN with zero for numeric columns.

    Key Points –

    <a id="h-quick-examples-of-dropping-rows-with-nan-values"></a>

    Quick Examples of Dropping Rows with NaN Values

    Below are quick examples of dropping rows with nan values.

    
# Quick examples of drop rows with nan values
  
# Example 1: Drop all rows with NaN values
df2=df.dropna()
df2=df.dropna(axis=0)

# Example 2: Reset index after drop
df2=df.dropna().reset_index(drop=True)

# Example 3: Drop row that has all NaN values
df2=df.dropna(how='all')

# Example 4: Drop rows that has NaN values on selected columns
df2=df.dropna(subset=['Courses','Fee'])

# Example 5: With threshold, 
# Keep only the rows with at least 2 non-NA values.
df2=df.dropna(thresh=2)

# Example 6: Drop Rows with NaN Values inplace
df.dropna(inplace=True)

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    Usage of Pandas Drop Rows with NaN Values in DataFrame

    In real-world datasets, missing values are quite common. To remove all rows containing any NaN values in a Pandas DataFrame, you can simply use the dropna() method. This provides an easy way to clean your data by filtering out incomplete rows.

    To run some examples of drop rows with NaN values in Pandas DataFrame, let’s create a Pandas DataFrame using data from a dictionary.

    
import pandas as pd
import numpy as np
technologies = ({
     'Courses':["Spark",'Java',"Hadoop",'Python',np.nan],
     'Fee' :[20000,np.nan,26000,24000,np.nan],
     'Duration':['30days',np.nan,'35days','40days',np.nan],
     'Discount':[1000,np.nan,2500,None,np.nan]
               })
df = pd.DataFrame(technologies)
print("Create DataFrame:\n", df)

    Yields below output.

    <a id="h-drop-rows-with-nan-values"></a>

    Drop Rows with NaN Values

    You can use the dropna() method to remove rows with NaN (Not a Number) and None values from Pandas DataFrame. By default, it removes any row containing at least one NaN value and returns the copy of the DataFrame after removing rows. If you want to remove from the existing DataFrame, you should use inplace=True.

    With NaN values in a Pandas DataFrame.

    
# Drop all rows that have NaN/None values
df2 = df.dropna()
print("After dropping the rows with NaN Values:\n", df2)

    Yields below output.

    Related: you can use the dropna(axis=1) to drop all columns with NaN values from DataFrame.

    Post-dropping rows with NaN, sometimes you may be required to reset the index, you can do so using the DataFrame.reset_index() method.

    
# Reset index after drop
df2 = df.dropna().reset_index(drop=True)
print("Reset the index after dropping:\n", df2)

    Yields below output.

    
# Output:
# Reset the index after dropping
  Courses      Fee Duration  Discount
0   Spark  20000.0   30days    1000.0
1  Hadoop  26000.0   35days    2500.0

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    Drop NaNs for all Columns in the DataFrame

    Similarly, you can use how parameter of the dropna() function to specify which rows to drop based on NaN values. By default, the Param how=any specifies all rows with NaN/None values on any element are removed.

    You can use how='all' to remove rows that have all NaN/None values in a row(data is missing for all elements in a row).

    
# Drop rows that has all NaN values
df2 = df.dropna(how='all')
print(" After dropping the rows which have all NaN values:\n", df2)

    Yields below output.

    
# Output:
# After dropping the rows which have all NaN values:
  Courses      Fee Duration  Discount
0   Spark  20000.0   30days    1000.0
1    Java      NaN      NaN       NaN
2  Hadoop  26000.0   35days    2500.0
3  Python  24000.0   40days       NaN

    <a id="h-drop-nan-values-on-selected-columns-from-list"></a>

    Drop NaN Values on Selected Columns from List

    Sometimes you may be required to drop rows only when selected columns have NaN/None values in DataFrame, you can achieve this by using subset param. This parameter takes a list of label names.

    
# Drop rows that has NaN values on selected columns
df2=df.dropna(subset=['Courses','Fee'])
print("After dropping rows based on specified columns:\n", df2)

    Yields below output.

    
# Output:
# After dropping rows based on specified columns:
  Courses      Fee Duration  Discount
0   Spark  20000.0   30days    1000.0
2  Hadoop  26000.0   35days    2500.0
3  Python  24000.0   40days       NaN

    <a id="h-drop-rows-with-nan-values-inplace"></a>

    Drop Rows with NaN Values inplace

    As you can see, by default dropna() method doesn’t drop rows from the original DataFrame; instead, it returns a copy of the DataFrame. If you intend to modify the existing DataFrame directly, you can set inplace=True.

    
# Drop Rows with NaN Values inplace
df.dropna(inplace=True)
print("After dropping the rows with NaN values:\n", df)

# Output:
# After dropping the rows with NaN values:
#   Courses      Fee Duration  Discount
# 0   Spark  20000.0   30days    1000.0
# 2  Hadoop  26000.0   35days    2500.0

    <a id="h-complete-example-of-drop-rows-with-nan-values"></a>

    Complete Example of Drop Rows with NaN Values

    
import pandas as pd
import numpy as np
technologies = ({
     'Courses':["Spark",'Java',"Hadoop",'Python',np.nan],
     'Fee' :[20000,np.nan,26000,24000,np.nan],
     'Duration':['30days',np.nan,'35days','40days',np.nan],
     'Discount':[1000,np.nan,2500,None,np.nan]
               })
df = pd.DataFrame(technologies)
print(df)

# Drop all rows with NaN values
df2=df.dropna()
print(df2)
df2=df.dropna(axis=0)

# Reset index after drop
df2=df.dropna().reset_index(drop=True)
print(df2)

# Drop row that has all NaN values
df2=df.dropna(how='all')
print(df2)

# Drop rows that has null on selected columns
df2=df.dropna(subset=['Courses','Fee'])
print(df2)

# Drop Rows with NaN Values inplace
df.dropna(inplace=True)
print(df)

    <a id="h-faq-on-drop-rows-with-nan-values"></a>

    FAQ on Drop Rows with NaN Values

    You can use the dropna() method to remove rows with NaN values in a Pandas DataFrame. By default, it removes any of the rows having at least one NaN value. For instance, df.dropna().

    df.dropna() which returns a new DataFrame with rows that don't have NaN values.

    You can use the subset parameter of the dropna() method to specify a subset of columns to consider for NaN removal. For example, df.dropna(subset=['specified_column'])

    You can specify multiple columns in the subset parameter. For example, df.dropna(subset=['specified_column1', 'specified_column2'])

    You can use the how parameter with the value 'all' to drop rows where all values are NaN. For example, df.dropna(how='all')

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    Conclusion

    In this article, I have explained dropping rows with NaN/None values in pandas DataFrame using DataFrame.dropna(). Also learned to remove rows only when all values are NaN/None, remove only when selected columns have NaN values, and remove using the inplace parameter.

    Happy Learning !!

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    References