Introduction to pandas Library in Python (Tutorial & Examples) | DataFrame Manipulation & Analysis

How to use the pandas library in the Python programming language. More details: https://statisticsglobe.com/pandas-library-python
Python code of this video:

import pandas as pd # Import pandas library to Python

data1 = pd.DataFrame() # Create empty DataFrame
print(data1) # Print empty DataFrame

data2 = pd.DataFrame({'x1':[5, 1, 2, 7, 5, 4], # Create pandas DataFrame with values
'x2':range(1, 7),
'x3':['a', 'b', 'a', 'c', 'b', 'c']})
print(data2) # Print pandas DataFrame

data3 = data2.drop('x2', axis = 1) # Apply drop() function
print(data3) # Print new pandas DataFrame

data4 = data2[['x1', 'x2']] # Subset columns of pandas DataFrame
print(data4) # Print new pandas DataFrame

new_col = [9, 99, 999, 99, 9, 999] # Create list
print(new_col) # Print list

data5 = data4.assign(new_col = new_col) # Add list as new column to pandas DataFrame
print(data5) # Print new pandas DataFrame

data6 = data5.copy() # Create copy of pandas DataFrame

print(data6.dtypes) # Print types of all columns

data6['new_col'] = data6['new_col'].astype(str) # Convert column to string
print(data6) # Print new pandas DataFrame

print(data6.dtypes) # Print types of all columns

data7 = pd.DataFrame({'ID':range(1001, 1007), # Create first pandas DataFrame with ID
'x1':[5, 1, 2, 7, 5, 4],
'x2':range(1, 7),
'x3':['a', 'b', 'a', 'c', 'b', 'c']})
print(data7) # Print pandas DataFrame

data8 = pd.DataFrame({'ID':range(1004, 1011), # Create second pandas DataFrame with ID
'y1':range(10, 3, - 1),
'y2':['x', 'y', 'y', 'x', 'x', 'y', 'x']})
print(data8) # Print pandas DataFrame

data9 = pd.merge(data7, # Inner join
data8,
on = 'ID')
print(data9) # Print merged pandas DataFrame

data10 = pd.merge(data7, # Outer join
data8,
on = 'ID',
how = 'outer')
print(data10) # Print merged pandas DataFrame

data11 = data9.copy() # Create copy of pandas DataFrame
data11.columns = ['ID', 'col1', 'col2', 'col3', 'col4', 'col5'] # Rename column names of pandas DataFrame
print(data11) # Print updated pandas DataFrame

data12 = data2.copy() # Create copy of pandas DataFrame
data12 = data12[data12.x1 != 5] # Drop by logical condition
print(data12) # Print updated pandas DataFrame

The remaining Python code can be found here: https://statisticsglobe.com/pandas-library-python

Table of Contents:
00:00 - Introduction
01:10 - Data Manipulation
18:10 - Data Analysis
21:26 - Data Visualization
24:02 - Data Export & Import

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