Pandas как найти медиану

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Python is a great language for doing data analysis, primarily because of the fantastic ecosystem of data-centric python packages. Pandas is one of those packages and makes importing and analyzing data much easier.

Pandas dataframe.median() function return the median of the values for the requested axis
If the method is applied on a pandas series object, then the method returns a scalar value which is the median value of all the observations in the dataframe. If the method is applied on a pandas dataframe object, then the method returns a pandas series object which contains the median of the values over the specified axis.

Syntax:DataFrame.median(axis=None, skipna=None, level=None, numeric_only=None, **kwargs)
Parameters :
axis : Align object with threshold along the given axis.
skipna : Exclude NA/null values when computing the result
level : If the axis is a MultiIndex (hierarchical), count along a particular level, collapsing into a Series
numeric_only : Include only float, int, boolean columns. If None, will attempt to use everything, then use only numeric data. Not implemented for Series.

Returns : median : Series or DataFrame (if level specified)

Example #1: Use median() function to find the median of all the observations over the index axis.

Lets use the dataframe.median() function to find the median over the index axis

Output :

 
Example #2: Use median() function on a dataframe which has Na values. Also find the median over the column axis.

Lets implement the median function.

Output :

Last Updated :
24 Nov, 2018

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You can use the following functions to calculate the mean, median, and mode of each numeric column in a pandas DataFrame:

print(df.mean(numeric_only=True))
print(df.median(numeric_only=True))
print(df.mode(numeric_only=True))

The following example shows how to use these functions in practice.

Suppose we have the following pandas DataFrame that contains information about points scored by various basketball players in four different games:

import pandas as pd

#create DataFrame
df = pd.DataFrame({'player': ['A', 'B', 'C', 'D', 'E', 'F', 'G', 'H'],
                   'game1': [18, 22, 19, 14, 14, 11, 20, 28],
                   'game2': [5, 7, 7, 9, 12, 9, 9, 4],
                   'game3': [11, 8, 10, 6, 6, 5, 9, 12],
                   'game4': [9, 8, 10, 9, 14, 15, 10, 11]})
                   
#view DataFrame
print(df)

  player  game1  game2  game3  game4
0      A     18      5     11      9
1      B     22      7      8      8
2      C     19      7     10     10
3      D     14      9      6      9
4      E     14     12      6     14
5      F     11      9      5     15
6      G     20      9      9     10
7      H     28      4     12     11

We can use the following syntax to calculate the mean value of each numeric column:

#calculate mean of each numeric column
print(df.mean(numeric_only=True))

game1    18.250
game2     7.750
game3     8.375
game4    10.750
dtype: float64

From the output we can see:

• The mean value in the game1 column is 18.25.
• The mean value in the game2 column is 7.75.
• The mean value in the game3 column is 8.375.
• The mean value in the game4 column is 10.75.

We can then use the following syntax to calculate the median value of each numeric column:

#calculate median of each numeric column
print(df.median(numeric_only=True))

game1    18.5
game2     8.0
game3     8.5
game4    10.0
dtype: float64

From the output we can see:

• The median value in the game1 column is 18.5.
• The median value in the game2 column is 8.
• The median value in the game3 column is 8.5.
• The median value in the game4 column is 10.

We can then use the following syntax to calculate the mode of each numeric column:

#calculate mode of each numeric column
print(df.mode(numeric_only=True))

   game1  game2  game3  game4
0   14.0    9.0    6.0      9
1    NaN    NaN    NaN     10

From the output we can see:

• The mode in the game1 column is 14.
• The mode in the game2 column is 9.
• The mode in the game3 column is 6.
• The mode in the game4 column is 9 and 10

Note that the game4 column had two modes since there were two values that occurred most frequently in that column.

Note: You can also use the describe() function in pandas to generate more descriptive statistics for each column.

Additional Resources

The following tutorials explain how to perform other common operations in pandas:

How to Calculate the Mean by Group in Pandas
How to Calculate the Median by Group in Pandas
How to Calculate Mode by Group in Pandas

The median of a set of numbers represents the middle value if the numbers are arranged in sorted order. It is a measure of central tendency and is often preferred over the mean as it’s not much affected by the presence of outliers. In this tutorial, we will look at how to get the median of one or more columns in a pandas dataframe.

How to calculate the median of pandas column values?

You can use the pandas median() function or the pandas quantile() function to get the median of column values in a pandas dataframe. The following is the syntax:

# median of single column
df['Col'].median()
# median of single column with quantile()
df['Col'].quantile(0.5)
# median of all numerical columns in dataframe
df.median()
# median of all numerical columns in dataframe with quantile()
df.quantile(0.5)

Let’s create a sample dataframe that we will be using throughout this tutorial to demonstrate the usage of the methods and syntax mentioned.

import pandas as pd

# create a dataframe
df = pd.DataFrame({
    'sepal_legth': [5.1, 4.9, 4.7, 4.6, 5.0, 5.4, 4.6, 5.0],
    'sepal_width': [3.5, 3.0, 3.2, 3.1, 3.6, 3.9, 3.4, 3.4],
    'petal_length': [1.4, 1.4, 1.3, 1.5, 1.4, 1.7, 1.4, 1.5],
    'petal_width': [0.2, 0.2, 0.2, 0.2, 0.2, 0.4, 0.3, 0.2],
    'sepices': ['setosa']*8
})
# display the dataframe
print(df)

Output:

   sepal_legth  sepal_width  petal_length  petal_width sepices
0          5.1          3.5           1.4          0.2  setosa
1          4.9          3.0           1.4          0.2  setosa
2          4.7          3.2           1.3          0.2  setosa
3          4.6          3.1           1.5          0.2  setosa
4          5.0          3.6           1.4          0.2  setosa
5          5.4          3.9           1.7          0.4  setosa
6          4.6          3.4           1.4          0.3  setosa
7          5.0          3.4           1.5          0.2  setosa

The sample dataframe is taken form a section of the Iris dataset. This sample has petal and sepal dimensions of eight data points of the “Setosa” species.

Median of a single column

First, let’s see how to get the median of a single dataframe column.

You can use the pandas series median() function to get the median of individual columns (which essentially are pandas series). For example, let’s get the median of the “sepal_length” column in the above dataframe.

# median of sepal_length column
print(df['sepal_length'].median())

Output:

4.95

You see that we get the median of all values in the “sepal_length” column as the scaler value 4.95.

Median of a single column using quantile()

Additionally, you can also use pandas quantile() function which gives the nth percentile value. Median is the 50th percentile value. So, to get the median with the quantile() function, pass 0.5 as the argument.

# median of sepal_length column using quantile()
print(df['sepal_length'].quantile(0.5))

Output:

4.95

Median of more than one column

Use the pandas dataframe median() function to get the median values for all the numerical columns in the dataframe. For example, let’s get the median of all the numerical columns in the dataframe “df”

# mean of multiple columns
print(df.median())

Output:

sepal_length    4.95
sepal_width     3.40
petal_length    1.40
petal_width     0.20
dtype: float64

We get the result as a pandas series.

Median of more than one column using quantile()

Additionally, you can use the pandas dataframe quantile() function with an argument of 0.5 to get the median of all the numerical columns in a dataframe. Let’s use this function on the dataframe “df” created above.

# mean of multiple columns using quantile()
print(df.quantile(0.5))

Output:

sepal_length    4.95
sepal_width     3.40
petal_length    1.40
petal_width     0.20
Name: 0.5, dtype: float64

You can see that we get the median of all the numerical columns present in the dataframe.

Note that you can also use the pandas describe() function to look at key statistics including the median values of the numerical columns in the dataframe.

# get dataframe statistics
df.describe()

Output:

The median here is represented by the 50% value (that is, the value at the 50th percentile).

For more on the pandas dataframe median() function, refer to its documention.

With this, we come to the end of this tutorial. The code examples and results presented in this tutorial have been implemented in a Jupyter Notebook with a python (version 3.8.3) kernel having pandas version 1.0.5

Tutorials on getting statistics for pandas dataframe values –

• Pandas – Get Mean of one or more Columns
• Pandas – Get Standard Deviation of one or more Columns
• Pandas – Get Median of One or More Columns
• Get correlation between columns of Pandas DataFrame
• Cumulative Sum of Column in Pandas DataFrame
• Pandas – Count Missing Values in Each Column
• Get Rolling Window estimates in Pandas
• Get the number of rows in a Pandas DataFrame
• Pandas – Count of Unique Values in Each Column

You can use the median() function to find the median of one or more columns in a pandas DataFrame:

#find median value in specific column
df['column1'].median()

#find median value in several columns
df[['column1', 'column2']].median()

#find median value in every numeric column
df.median()

The following examples show how to use this function in practice with the following pandas DataFrame:

#create DataFrame
df = pd.DataFrame({'player': ['A', 'B', 'C', 'D', 'E', 'F', 'G', 'H'],
                   'points': [25, pd.NA, 15, 14, 19, 23, 25, 29],
                   'assists': [5, 7, 7, 9, 12, 9, 9, 4],
                   'rebounds': [11, 8, 10, 6, 6, 5, 9, 12]})

#view DataFrame
df

	player	points	assists	rebounds
0	A	25	5	11
1	B	NA	7	8
2	C	15	7	10
3	D	14	9	6
4	E	19	12	6
5	F	23	9	5
6	G	25	9	9
7	H	29	4	12

Example 1: Find Median of a Single Column

The following code shows how to find the median value of a single column in a pandas DataFrame:

#find median value of points column
df['points'].median()

23.0

The median value in the points column is 23. 

Note that by default, the median() function ignores any missing values when calculating the median.

Example 2: Find Median of Multiple Columns

The following code shows how to find the median value of multiple columns in a pandas DataFrame:

#find median value of points and rebounds columns
df[['points', 'rebounds']].median()

points      23.0
rebounds     8.5
dtype: float64

Example 3: Find Median of All Numeric Columns

The following code shows how to find the median value of all numeric columns in a pandas DataFrame:

#find median value of all numeric columns
df.median()

points      23.0
assists      8.0
rebounds     8.5
dtype: float64

Additional Resources

How to Calculate the Mean of Columns in Pandas
How to Calculate the Sum of Columns in Pandas
How to Find the Max Value of Columns in Pandas