Pandas: Summary Statistics

Summary Statistics

•  Aggregation means combining multiple data items from the data set into a single value. Several commonly used functions like sum(), mean(), avg() are used for aggregation of data.

• Pandas provide various library functions to perform aggregation operations. Some of the functions used in aggregation are as follows ‒

sum() ‒ Compute sum of column values

max() ‒ Compute max of column values

size() ‒ Compute column sizes

first() ‒ Compute first of group values

count() ‒ Compute count of column values

var() ‒ Compute variance of column

min() ‒ Compute min of column values

mean() ‒ Compute mean of column

describe() ‒ Generates descriptive statistics

last() ‒ Compute last of group values

std() ‒ Standard deviation of column

sem() ‒ Standard error of the mean of column

For example ‒

•  The sum() function calculates the sum of every value. We can find the total weights of females and total weights of males using sum() function. The code is as follows ‒

In [4]: import pandas as pd

data = {'Gender': ['f', 'm', 'f', 'm', 'm', 'f', 'm'], 'Weight : [45,71,69,73,80,55,98]}

df = pd.DataFrame(data)

print(df)

f = df[ 'Gender'] = = 'f'

female wt = df[f]['Weight'].sum()

print("‒‒‒‒‒‒‒‒‒‒‒‒‒‒‒‒‒‒‒‒‒‒‒‒‒‒‒‒‒‒‒‒‒‒‒‒‒‒‒‒‒‒‒‒‒‒‒")

print("Total weight of all females is: ", female_wt)

m = df['Gender'] = = 'm'

male wt ‒ df[m]['weight'].sum()

print("Total weight of all males is: ",male_wt)

Output

• The describe() function gives the summary of our data set. The code is

In [5]: import pandas as pd

data = {'Gender': ['f', 'm', 'f', , 'm', 'm', 'f', 'm'], 'Weight':[45,71,69,73,80,55,98]}

df = pd.DataFrame(data)

print(df)

df.describe()

Output

• The agg() method allows to apply a function or a list of function names to be executed. The agg() method is an alias of the aggregate() method. The agg() function is used to calculate sum, min and max of each column in the data set.

• For example ‒

import pandas as pd

data = {'Gender': ['f','m','f', 'm','m', 'f','m'], 'Weight': [45,71,69,73,80,55,98]}

df = pd.DataFrame(data)

print(df)

df.agg(['sum','min','max'])

Output