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    Pandas DataFrame median() Method

    Pandas DataFrame median() Method

    In Pandas, the median() method is used to compute the median of values for each column or row in a DataFrame, excluding NA/null values by default.

    In this article, I will explain the Pandas DataFrame median() method by using its syntax, parameters, and usage, and how to return the median of the values along the specified axis.

    Key Points –

    <a id="h-syntax-of-pandas-dataframe-median-method"></a>

    Syntax of Pandas DataFrame median() Method

    Let’s know the syntax of the pandas DataFrame.median() method.

    
# Syntax of DataFrame.median() method
DataFrame.median(axis=0, skipna=True, level=None, numeric_only=None, **kwargs)

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    Parameters of the median()

    Following are the parameters of the median() method

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    Return Value

    It returns the method returns a Series object containing the median values for the requested axis.

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    Usage of Pandas DataFrame median() Method

    The median() method in Pandas is used to calculate the median (the middle value) of numeric data along a specified axis of a DataFrame.

    To run some examples of pandas DataFrame median() method, Let’s create Pandas DataFrame using data from a Python dictionary, where the columns are A , B and C.

    
import pandas as pd

# Sample DataFrame
df = pd.DataFrame({
    'A': [2, 4, 6, 8, 10],
    'B': [1, 3, 5, 7, 9],
    'C': [4, 6, 1, 9, 3]
})
print("Original DataFrame:\n",df)

    Yields below output.

    <a id="h-column-wise-median-default"></a>

    Column-wise Median (Default)

    To compute the column-wise median of a DataFrame (which is the default behavior of the median() method), you can simply call the median() method without specifying the axis parameter, as it defaults to axis=0.

    
# Compute the median of each column
column_medians = df.median()
print("Column-wise medians:\n", column_medians)

    Here,

    <a id="h-row-wise-median"></a>

    Row-wise Median

    Alternatively, to compute the median for each row in a DataFrame, you need to specify the axis parameter as 1 in the median() method.

    
# Compute the median of each row
row_medians = df.median(axis=1)
print("Row-wise medians:\n", row_medians)

# Output:
# Row-wise medians:
# 0    2.0
# 1    4.0
# 2    5.0
# 3    8.0
# 4    9.0
# dtype: float64

    Here,

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    Handling Missing Values (skipna=True)

    When calculating the median in a DataFrame and handling missing values, the skipna=True parameter (which is the default setting) ensures that NA/null values are excluded from the computation. This means that missing values are ignored, and the median is calculated based only on the available data.

    
import pandas as pd
import numpy as np

# Sample DataFrame with missing values
df = pd.DataFrame({
    'A': [2, None, 6, 8, 10],
    'B': [1, 3, None, 7, 9],
    'C': [4, 6, 1, 3, None]
})

# Compute the median of each column, excluding NA/null values
df2 = df.median(skipna=True)
print("Column-wise medians excluding NA values:\n", df2)

# Output:
# Column-wise medians excluding NA values:
# A    7.0
# B    5.0
# C    3.5
# dtype: float64

    Here,

    <a id="h-handling-missing-values-skipna-false"></a>

    Handling Missing Values (skipna=False)

    Similarly, when calculating the median and setting skipna=False, the presence of missing values (NA/null) will affect the result. If any NA/null values are present in the data, the result will be NA, as the median cannot be computed properly when there are missing values.

    
# Compute the median of each column, including NA/null values
df2 = df.median(skipna=False)
print("Column-wise medians including NA values:\n", df2)

# Output:
# Column-wise medians including NA values:
# A   NaN
# B   NaN
# C   NaN
# dtype: float64

    Here,

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    Median for Numeric Data Only

    Finally, you can compute the median only for numeric columns in a DataFrame, use the numeric_only=True parameter with the median() method. This ensures that non-numeric columns are excluded from the calculation.

    
import pandas as pd

# Sample DataFrame with mixed data types
df = pd.DataFrame({
    'A': [2, 4, 'Pandas', 8, 10],
    'B': [1, 3, 5, 7, 9],
    'C': [4, 6, 1, 9, 3]
})

# Compute the median, including only numeric data
df2 = df.median(numeric_only=True)
print("Medians for numeric data only:\n", df2)

# Output:
# Medians for numeric data only:
# B    5.0
# C    4.0
# dtype: float64

    Here,

    <a id="h-median-along-a-specific-level-multiindex-dataframe"></a>

    Median along a Specific Level (MultiIndex DataFrame)

    Finally, to compute the median along a specific level in a MultiIndex DataFrame, you can use the level parameter in the median() method. This is useful when working with hierarchical data and you want to calculate the median for each group defined by a particular level of the MultiIndex.

    
import pandas as pd

# Sample MultiIndex DataFrame
df_multi = pd.DataFrame({
    'A': [2, 4, 6, 8],
    'B': [1, 3, 5, 7],
    'C': [4, 6, 1, 9]
}, index=pd.MultiIndex.from_tuples([('X', 1), ('X', 2), ('Y', 1), ('Y', 2)], names=['group', 'subgroup']))
print("Original MultiIndex DataFrame:\n", df_multi)

# Compute the median along the 'group' level
df2 = df_multi.median(level='group')
print("Median by group:\n", df2)

# Output:
# Original MultiIndex DataFrame:
#                  A  B  C
# group subgroup         
# X     1         2  1  4
#       2         4  3  6
# Y     1         6  5  1
#       2         8  7  9

# Median by group:
#         A  B  C
# group         
# X      3  2  5
# Y      7  6  5

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    Frequently Asked Questions on Pandas DataFrame median() Method

    The median() method calculates the median of the numeric columns in a DataFrame. By default, it computes the median for each column (axis=0).

    By default, the median() method excludes missing values (NA/null) from the calculation with skipna=True. To include missing values and get NA as the result if any are present, set skipna=False.

    The median() method is designed to work with numeric data. Non-numeric columns are ignored unless explicitly included using numeric_only=False.

    The median() method can only compute the median for either rows or columns in a single call. You need to call the method twice, once with axis=0 and once with axis=1, to get both row-wise and column-wise medians.

    If a column contains mixed data types (e.g., numeric and strings), the method will ignore the entire column when calculating the median, unless numeric_only=False is set.

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    Conclusion

    In conclusion, the Pandas median() method is an essential tool for statistical analysis within DataFrames. It provides flexibility in calculating medians across different axes and handling missing values effectively.

    Happy Learning!!

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    Related Articles

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    References