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Summary Python Data Operations 4: Merge and concatenation
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  • Course
  • Programming
  • Institution
  • IE University
  • Book
  • Learn Python 3 the Hard Way

Summarised notes of pandas data operations covered in the Principles of Programming course, part of the Computer Science and AI bachelor degree. The notes are initially written in Jupyter Notebook. They contain practical examples of data operations in python and images to explain the structures and...

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  • No
  • Pandas data operations
  • December 9, 2022
  • 8
  • 2022/2023
  • Summary

Subjects

  • python
  • data operations
  • data wrangling
  • merge
  • join
  • dataframe
  • data analysis
  • data analytics
  • data processes
  • principles of programming
  • computer science
  • pandas
  • panda
  • python basics
  • summarised notes
  • summart
  • no

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Python Data Operations 4: Merge and Concat
(Using the numpy and pandas packages imported in section one.)

This fourth section contains:

Concatenation
Merge and Join

Merge on columns
Merge on index
Convert Data types
Duplicates

Find duplicates
Remove duplicates



Concat
The .concat function joins two datasets by placing them on top of each other (vertical
concatenation) or next to each other (horizontal concatenation).

The first parameter is a list of the DataFrames to be concatenated
The second parameter (axis) is a value indicating the direction: vertical (0) or
horizontal (1)




#creating test df
test_df = pd.DataFrame(
[['A3', 0, -1, 0, 'si'],
['B1', 1, None, 0, 'no'],
['B3', 4, None, 0, 'no'],
['B3', 5, 1, 0, 'si'],
['A1', 4, 0, None, None],
['A3', 1, 2, 1, 'si'],
['C2', 4, 1, 1, 'no']],

, columns=['A', 'B', 'C', 'D', 'E'],
index=[f'R{i}' for i in range(7)]
)
test_df


A B C D E

R0 A3 0 -1.0 0.0 si

R1 B1 1 NaN 0.0 no

R2 B3 4 NaN 0.0 no

R3 B3 5 1.0 0.0 si

R4 A1 4 0.0 NaN None

R5 A3 1 2.0 1.0 si

R6 C2 4 1.0 1.0 no


#creating second test df
test_df2 = pd.DataFrame(
[['A1', 100],
['B1', -50],
['C1', -25]],
columns=['A', 'Z'],
index=['R0', 'R7', 'R14'])
test_df2


A Z

R0 A1 100

R7 B1 -50

R14 C1 -25



# concat 3 dfs vertically
pd.concat([test_df, test_df2, test_df2])

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