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Summary Python Data Operations Notes
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  • Course
  • Principles of Programming
  • Institution
  • IE University

Notes of python data operations using pandas, covered in the Principles of Programming course, part of the Computer Science and AI bachelor degree. The notes are initially written in Jupyter Notebook, here in pdf format. They contain practical examples of data operations in python and images to exp...

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Document information

  • December 8, 2022
  • 15
  • 2022/2023
  • Summary

Subjects

  • pandas
  • data
  • python
  • python data operations
  • data wrangling
  • data analysis
  • dataframes
  • jupyter notebook
  • programming
  • principles of programming
  • computer science
  • business analytics
  • data analytics
  • panda
  • pandas

Written for

  • IE University
  • Computer Science
  • Principles of Programming
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Pandas Data Operations

import pandas as pd
import numpy as np



What is a Dataframe?
A dataframe is a data type provided by the library pandas
It is the most relevant data type to work with tables and data in python
Imagine dataframe as a table created by rows and colummns where each row and column
is an object type pandas.Series (vector/list). Each element contains a label.



Create a DataFrame
Adding data manually

Lists of lists
Nested dictionaries
Reading the information from .csv file

Using the function pd.read_csv() with the path of the file.


#create 2D array with data
data_lst = [
['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']
]

data_lst

[['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']]


#print first column
col0 = []

, for row in data_lst:
col0.append(row[0])

col0

['A3', 'B1', 'B3', 'B3', 'A1', 'A3', 'C2']

#create test dataframe
test_df = pd.DataFrame(
data_lst
)
test_df


0 1 2 3 4

0 A3 0 -1.0 0.0 si

1 B1 1 NaN 0.0 no

2 B3 4 NaN 0.0 no

3 B3 5 1.0 0.0 si

4 A1 4 0.0 NaN None

5 A3 1 2.0 1.0 si

6 C2 4 1.0 1.0 no



#update index of rows and columns
test_df = pd.DataFrame(
data_lst,
columns=['A', 'B', 'C', 'D', 'E'],
index=[f'row{i}' for i in range(1, 8)]
)
test_df


A B C D E

row1 A3 0 -1.0 0.0 si

row2 B1 1 NaN 0.0 no

row3 B3 4 NaN 0.0 no

row4 B3 5 1.0 0.0 si

row5 A1 4 0.0 NaN None

row6 A3 1 2.0 1.0 si

row7 C2 4 1.0 1.0 no




DataFrame structure

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