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

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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
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
print(data5) # Print new pandas DataFrame
data6['new_col'] = data6['new_col'].astype(str) # Convert column to string
print(data6) # Print new pandas DataFrame
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
data8,
on = 'ID')
print(data9) # Print merged pandas DataFrame
data8,
on = 'ID',
how = 'outer')
print(data10) # Print merged pandas DataFrame
print(data11) # Print updated pandas DataFrame
data12 = data12[data12.x1 != 5] # Drop by logical condition
print(data12) # Print updated pandas DataFrame
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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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
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
print(data5) # Print new pandas DataFrame
data6['new_col'] = data6['new_col'].astype(str) # Convert column to string
print(data6) # Print new pandas DataFrame
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
data8,
on = 'ID')
print(data9) # Print merged pandas DataFrame
data8,
on = 'ID',
how = 'outer')
print(data10) # Print merged pandas DataFrame
print(data11) # Print updated pandas DataFrame
data12 = data12[data12.x1 != 5] # Drop by logical condition
print(data12) # Print updated pandas DataFrame
Table of Contents:
00:00 - Introduction
01:10 - Data Manipulation
18:10 - Data Analysis
21:26 - Data Visualization
24:02 - Data Export & Import
Follow me on Social Media:
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