Mathematically central tendency means measuring the center or distribution of location of values of a data set. It gives an idea of the average value of the data in the data set and also an indication of how widely the values are spread in the data set. That in turn helps in evaluating the chances of a new input fitting into the existing data set and hence probability of success.
There are three main measures of central tendency which can be calculated using the methods in pandas python library.
- Mean – It is the Average value of the data which is a division of sum of the values with the number of values.
- Median – It is the middle value in distribution when the values are arranged in ascending or descending order.
- Mode – It is the most commonly occurring value in a distribution.
Calculating Mean and Median
The pandas functions can be directly used to calculate these values.
import pandas as pd
#Create a Dictionary of series
d = {'Name':pd.Series(['Tom','James','Ricky','Vin','Steve','Smith','Jack',
'Lee','Chanchal','Gasper','Naviya','Andres']),
'Age':pd.Series([25,26,25,23,30,29,23,34,40,30,51,46]),
'Rating':pd.Series([4.23,3.24,3.98,2.56,3.20,4.6,3.8,3.78,2.98,4.80,4.10,3.65])}
#Create a DataFrame
df = pd.DataFrame(d)
print "Mean Values in the Distribution"
print df.mean()
print "*******************************"
print "Median Values in the Distribution"
print df.median()
Its output is as follows −
Mean Values in the Distribution Age 31.833333 Rating 3.743333 dtype: float64 ******************************* Median Values in the Distribution Age 29.50 Rating 3.79 dtype: float64
Calculating Mode
The mode may or may not be available in distribution depending on whether the data is continous or whether there are values which has maximum frequency. We take a simple distribution below to find out the mode. Here we have a value that has a maximum frequency in the distribution.
import pandas as pd
#Create a Dictionary of series
d = {'Name':pd.Series(['Tom','James','Ricky','Vin','Steve','Smith','Jack',
'Lee','Chanchal','Gasper','Naviya','Andres']),
'Age':pd.Series([25,26,25,23,30,25,23,34,40,30,25,46])}
#Create a DataFrame
df = pd.DataFrame(d)
print df.mode()
Its output is as follows −
Age Name 0 25.0 Andres 1 NaN Chanchal 2 NaN Gasper 3 NaN Jack 4 NaN James 5 NaN Lee 6 NaN Naviya 7 NaN Ricky 8 NaN Smith 9 NaN Steve 10 NaN Tom 11 NaN Vin
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