How to Get Column Average or Mean in pandas DataFrame

To get column average or mean from pandas DataFrame use either mean() and describe() method. The DataFrame.mean() method is used to return the mean of the values for the requested axis. If you apply this method on a series object, then it returns a scalar value, which is the mean value of all the observations in the pandas DataFrame.

Related: Get all column names from pandas DataFrame

In this article, I will explain how to get column average or mean from pandas DataFrame with examples.

1. Quick Examples of Get Column Average Or Mean In DataFrame

Below are some quick examples of how to get column average or mean in pandas DataFrame.

# Below are the quick examples

# Using DataFrame.mean() method to get column average
df2 = df["Fee"].mean()

# Using DataFrame.mean() to get entire column mean
df2 = df.mean()

# Using multiple columns mean using DataFrame.mean()
df2 = df[["Fee","Discount"]].mean()

# Average of each column using DataFrame.mean()
df2 = df.mean(axis=0)

# Find the mean including NaN values using DataFrame.mean()
df2 = df.mean(axis = 0, skipna = False)

# Using DataFrame.describe() method
df2 = df.describe()

Now, let’s create a DataFrame with a few rows and columns, execute these examples and validate results. Our DataFrame contains column names Courses, Fee, Duration and Discount.

# Get Column Average Or Mean In DataFrame
import pandas as pd
technologies = {
    'Courses':["Spark","PySpark","Python","pandas",None],
    'Fee' :[20000,25000,22000,None,30000],
    'Duration':['30days','40days','35days','None','50days'],
    'Discount':[1000,2300,1200,2000,None]
              }
index_labels=['r1','r2','r3','r4','r5']
df = pd.DataFrame(technologies,index=index_labels)
print(df)

Yields below output.

# Output:
    Courses      Fee Duration  Discount
r1    Spark  20000.0   30days    1000.0
r2  PySpark  25000.0   40days    2300.0
r3   Python  22000.0   35days    1200.0
r4   pandas      NaN     None    2000.0
r5     None  30000.0   50days       NaN

2. Get Column Mean

DataFrame.mean() method gets the mean value of a particular column from pandas DataFrame, you can use the df["Fee"].mean() function for a specific column only.

# Using DataFrame.mean() method to get column average
df2 = df["Fee"].mean()
print(df2)

Yields below output.

# Output:
24250.0

4. Get Column Mean for All Columns

To calculate the mean of whole columns in the DataFrame, use pandas.Series.mean() with a list of DataFrame columns. You can also get the mean for all numeric columns using DataFrame.mean(), use axis=0 argument to calculate the column-wise mean of the DataFrame.

# Using DataFrame.mean() to get entire column mean
df2 = df.mean()
print(df2)

# Using multiple columns mean using DataFrame.mean()
df2 = df[["Fee","Discount"]].mean()
print(df2)

# Average of each column using DataFrame.mean()
df2 = df.mean(axis=0)
print(df2)

Above all examples yields the same below output.

# Output:
Fee         24250.0
Discount     1625.0
dtype: float64

5. Find the Mean Including NaN Values

By default mean() ignores/exclude NaN/null values while calculating mean or average, you can consider these values by using skipna=False param.

# Find the mean ignoring NaN values using DataFrame.mean()
df2 = df.mean(axis = 0, skipna = False)
print(df2)

I will leave it to you to execute this in your environment.

6. Using DataFrame.describe() Method

You can also use DataFrame.describe() to create the output of complete statistics of the data in DataFrame.

# Using DataFrame.describe() method
df2 = df.describe()
print(df2)

Yields below output.

# Output:
               Fee     Discount
count      4.00000     4.000000
mean   24250.00000  1625.000000
std     4349.32945   623.832242
min    20000.00000  1000.000000
25%    21500.00000  1150.000000
50%    23500.00000  1600.000000
75%    26250.00000  2075.000000
max    30000.00000  2300.000000

7. Complete Example For Get Column Average or Mean

# Example For Get Column Average or Mean
import pandas as pd
technologies = {
    'Courses':["Spark","PySpark","Python","pandas",None],
    'Fee' :[20000,25000,22000,None,30000],
    'Duration':['30days','40days','35days','None','50days'],
    'Discount':[1000,2300,1200,2000,None]
              }
index_labels=['r1','r2','r3','r4','r5']
df = pd.DataFrame(technologies,index=index_labels)
print(df)

# Using DataFrame.mean() method to get column average
df2 = df["Fee"].mean()
print(df2)

# Using DataFrame.mean() to get entire column mean
df2 = df.mean()
print(df2)

# Using multiple columns mean using DataFrame.mean()
df2 = df[["Fee","Discount"]].mean()
print(df2)

# Average of each column using DataFrame.mean()
df2 = df.mean(axis=0)
print(df2)

# Find the mean ignoring NaN values using DataFrame.mean()
df2 = df.mean(axis = 0, skipna = False)
print(df2)

# Using DataFrame.describe() method
df2 = df.describe()
print(df2)

Conclusion

In this article, you have learned how to get column average or mean from pandas DataFrame using DataFrame.mean() and DataFrame.describe() method with examples. Using mean() you can get mean from single or selected columns and by index.

Happy Learning !!

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References

• https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.mean.html