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Summary Python Data Operations 3: Filtering
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  • Course
  • Programming
  • Institution
  • IE University
  • Book
  • Learn Python 3 the Hard Way

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 processes....

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Document information

  • No
  • Pandas data wrangling
  • December 9, 2022
  • 10
  • 2022/2023
  • Summary

Subjects

  • pandas
  • data wrangling
  • data operations
  • dataframes
  • python
  • principles of programming
  • data analytics
  • data analysis
  • filter
  • filtering
  • nulls
  • nan values
  • unique values
  • methods
  • referen
  • summary
  • notes
  • study guide

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Python Data Operations 3: Filtering
(Using the numpy and pandas packages imported in section one.)

This third section contains:

Conditional Selection
pd.Series and Operators
Basic Filters
Missing Values

Identify Nulls
Filter Nulls
Fill in Nulls
Remove Nulls
Unique Values
# create test dataframe
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




Conditional selection
In pandas conditional selection is filtering some records according to certain criteria

, The syntax is df[filter] where filter is a sequence of boolean values of the same length
as the table, and the command allows us to select/filter records according to a certain
condition.



# create simple filter
filter = [True, True, False, False, True, True, False]
filter

[True, True, False, False, True, True, False]


# apply simple filter
# .iloc and .loc are equivalent here
test_df.iloc[filter, :]
test_df.loc[filter, :]


A B C D E

R0 A3 0 -1.0 0.0 si

R1 B1 1 NaN 0.0 no

R4 A1 4 0.0 NaN None

R5 A3 1 2.0 1.0 si



# filter columns containing a list of column names
columns = ['A', 'B', 'C']
# apply columns filter
test_df.loc[:, columns]
test_df[columns] #equivalent to previous line

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