Python: Basic Codes

SAS has been the leader in statistical softwares in large corporates and industries for a long time, while SPSS and STATA have been on top in academics. However, with the huge influx of start-ups, R and Python are coming up quickly to the top, and have challenged SAS for the leading position.

In this blog, I will share a basic set of Python commands and codes that will be helpful to start working with Python. These are easily available online, and this blog is just a small step to consolidate the important codes in one place. If someone really wants to learn Python, it is recommended to browse through coursera and edx.

I had shared similar codes about data exploration in R in a previous blog. You can read it here.

In this blog, we will go through the following commands:

1. Reading Data in Python
2. List of columns in a dataset
3. Appending two dataset
4. Different joins in Python
5. Where conditions and multiple where conditions
6. Group by command
7. Finding distinct values in a vector

There are multiple modules available in python, and I have used Pandas for the basic operations.

# Importing the pandas module in the memory

import pandas as pd

# 1. Reading a .csv file in Python

Tran_Table1 = pd.read_csv('C:\Users\ Desktop\Python_Dir\Tran_Table1.csv')

Tran_Table2 = pd.read_csv('C:\Users\ Desktop\Python_Dir\Tran_Table2.csv')

Cust_Details = pd.read_csv('C:\Users           \Desktop\Python_Dir\Cust_Details.csv')

# 2. The top 5 columns and column names for a data frame

Cust_Details.head()

# The list of column names for a data frame

Colname1 = list(Tran_Table1.columns.values)

Colname2 = list(Cust_Details.columns.values)

# 3. Merging (or appending) two data frames

frame1 = [Tran_Table1,Tran_Table2]

Total_Tran_Table = pd.concat(frame1)

# 4. Joins - Inner, Left, Right, Outer

Inner_Join = pd.merge(Tran_Table1, Cust_Details, how = 'inner', on = 'Cust_No')

Left_Join = pd.merge(Tran_Table1, Cust_Details, how = 'left', left_on = 'Cust_No', right_on = 'Cust_No')

Right_Join = pd.merge(Tran_Table1, Cust_Details, how = 'right', left_on = 'Cust_No', right_on = 'Cust_No')

Outer_Join = pd.merge(Tran_Table1, Cust_Details, how = 'outer', left_on = 'Cust_No', right_on = 'Cust_No')

# 5. 'Where' condition

Asian_Customer = Cust_Details[Cust_Details['Cust_Continent']=='Asia']

Asian_Customer

# Multiple 'Where' conditions

Asian_Male_Customer = Cust_Details[(Cust_Details['Cust_Continent']=='Asia') & (Cust_Details['Cust_Gender']=='Male')]

Asian_Male_Customer

# 6. Group By

Tran_Table1_Details = pd.merge(Tran_Table1, Cust_Details, how = 'inner', on = 'Cust_No')

Tran_Table1_Details.groupby('Cust_Continent')['X1'].sum()

Tran_Table1_Details.groupby(['Cust_Gender','Cust_Continent'])['Cust_No'].count()

# 7. Unique (distinct) values in a column

Unique_Continents = list(set(Cust_Details.Cust_Continent))