Descriptive Statistics with Python: Part 1
By: Arpita Ghosh
Tags: Women In Data Science, Statistics, Data Science, Python

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Introduction: To learn about data science or machine learning , it is basic necessity to know about statistics. Nowadays if anybody is attending data science or data analyst interview , it is obvious to face some questions related to statistics. If someone is new to data science world, it is very difficult for him/her to relate statistical application using python code.

In this blog I wrote python code with key notes related to descriptive statistics.

What is Statistics? To know about , we first know about to term . Population and Sample. Population is total data set collected for analysis , denoted as N. Sample is subset of population, denoted as n. Statistics is mathematical analysis and representation about this sample data. Parameter is the mathematical representation of population data.

For example, one company wants to conduct a survey on employee satisfaction for the entire company. You were tasked with contacting your project members about their opinion and then submitting them to the HR manager. Is it population or sample data? What should be the name of this presented value?

Answer: It is Sample data and presented value is called Statistics. Because you took only one project members’ data which is a small part of whole company data.

Types of Data Now we have to understand how many types of data are there. There are two types of data, categorical and numerical. Numerical data is divided into discrete and continuous.

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Now let’s start with python coding.

#Declare all required packages in jupyter notebook

import pandas as pd

import numpy as np

import matplotlib.pyplot as plt

import seaborn as sns

%matplotlib inline

from matplotlib.ticker

import PercentFormatter

# Type of Data : Categorical and Numerical

cate_list=["Apple","Banana","Orange"]

cate_list

Output: ['Apple', 'Banana', 'Orange']

cate_dict={1:"Apple",2:"Banana",3:"Orange"}

cate_dict

Output: {1: 'Apple', 2: 'Banana', 3: 'Orange'}

num_list=[1,2,6,7,4]

num_list

Output: [1, 2, 6, 7, 4]

print(np.random.rand(5))

print(np.random.randint(1,16,10))

# Type of Numerical data : Discrete and Continuous. Age is discrete #data, Month is continuous data.

# Convert dictionary to data frame

data={'Age': [20,25,20,35,40,45,50,55,60,65,70,75],'Month':[1,2,3,4,5,6,7,8,9,10,11,12]}

df1=pd.DataFrame.from_dict(data)

df1

Levels of Measurement There are two types of levels of measurement, Qualitative and Quantitative. Two qualitative levels: nominal and ordinal. There are two quantitative levels: interval and ratio.

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# Nominal Data

nominal_dict={'Gender': ['Female','Male'],'Hair_Color': ['Black','White']}

df2=pd.DataFrame.from_dict(nominal_dict)

df2

Output:

Gender Hair_Color

0 Female Black

1 Male White # Ordinal Data

ordinal_dict={'Rating': ['Satified','Avg Satisfied','Not Satisfied']}

df3=pd.DataFrame.from_dict(ordinal_dict)

df3

Output:

Rating

0 Satified

1 Avg Satisfied

2 Not Satisfied #Interval Data

interval_data={'Income':[25000,30000,40000]} df4=pd.DataFrame.from_dict(interval_data)

df4

Output:

Income

0 25000

1 30000

2 40000

# Ratio Data : measurement of heights.

ratio_data={'Height':[160,167,170]} df5=pd.DataFrame.from_dict(ratio_data)

df5

Output:

Height

0 160

1 167

2 170

Conclusion : Till now we talked about different types of variables, in next blog, will discuss about Central Tendency (end part of descriptive statistics) with Pythons.