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![Panel Data Example
•A three-dimensional data structure designed for handling 3D data.
•As of Pandas version 1.0.0, Panels are deprecated and users are encouraged to use multi-index DataFrames
import numpy as np
panel = pd.Panel(np.random.rand(2, 3, 4), items=['Item1', 'Item2'], major_axis=['A', 'B', 'C'], minor_axis=['X1', 'X2', 'X3', 'X4'])
print(panel)](https://image.slidesharecdn.com/session9-240828134001-df104403/85/Basic-data-manipulation-with-pandas-pandas-10-320.jpg)
![Panel Data Example
•A three-dimensional data structure designed for handling 3D data.
•As of Pandas version 1.0.0, Panels are deprecated and users are encouraged to use multi-index DataFrames
import numpy as np
panel = pd.Panel(np.random.rand(2, 3, 4), items=['Item1', 'Item2'], major_axis=['A', 'B', 'C'], minor_axis=['X1', 'X2', 'X3', 'X4'])
print(panel)](https://image.slidesharecdn.com/session9-240828134001-df104403/75/Basic-data-manipulation-with-pandas-pandas-10-2048.jpg)
Uploaded byLipika Sharma
Basic data manipulation with pandas pandas
This document provides an introduction to Python for data science, focusing on the Pandas library, which is used for data manipulation and analysis. It covers fundamental concepts such as Series, DataFrame, and Panel data structures, including their creation, characteristics, and methods for statistical analysis. It also includes installation instructions for Pandas and explains how to manage data effectively within these structures.
In this document
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An overview of the presentation focused on Python's application in Data Science.
Topics related to OOPS, Anaconda installation, Jupyter Notebook interface, and usage shortcuts.
Introduction to Pandas with agenda on Series, DataFrame, installation, and data manipulation techniques.
Pandas is a data manipulation library enabling analysis, cleaning, and exploration of datasets.
Description of Pandas Series as a 1D array-like structure for homogeneous data and its immutability.
DataFrame as a 2D structure in Pandas, framed in rows and columns, supporting various data types.
Panel as a 3D data structure used in Pandas, increasingly deprecated in favor of multi-index DataFrames.
Methods of installing Pandas, particularly using the Python package installer PIP.
Processes for creating DataFrames from dictionaries, adding data, and retrieving shape and information.
Procedures for conducting quantitative statistical analysis to summarize and analyze data.
Techniques for dropping columns and rows in a DataFrame using the 'drop' method.
An interactive Q&A section addressing key concepts in Pandas and its application.
Focused questions to test knowledge about Pandas Series, DataFrame installation, and data shape retrieval.
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Basic data manipulation with pandas pandas
- 1. Introduction to Pythonfor Data Science
- 2. Replay • OOPS Continued------ •Anaconda Installation • Jupyter Notebook Interface • Working and Use of Jupyter Notebook • Shortcuts for cell moving and marking
- 3. Session 9:Basic datamanipulation with pandas Agenda • What is Pandas Series DataFrame Panel • Installing Pandas • Creating DataFrame • Adding data in DataFrame using Append Function • Getting Shape and information of the data • Getting Statistical Analysis of Data • Dropping Columns from Data • Dropping Rows from Data
- 4. Pandas • Pandas isa Python library used for working with data sets. • It has functions for analyzing, cleaning, exploring, and manipulating data. • The name "Pandas" has a reference to both "Panel Data", and "Python Data Analysis" and was created by Wes McKinney in 2008.
- 5. Series • Pandas Seriesis a 1-dimensional structure resembling arrays containing homogeneous data in it. It is a linear data structure and stores elements in a single dimension. • Note: The size of the Series Data Structure in Pandas is immutable i.e once set, it cannot be changed dynamically. While the values/elements in the Series can be changed or manipulated.
- 6. Series Example • Aone-dimensional labeled array capable of holding any data type Output
- 7. DataFrame • Python Pandasmodule provides DataFrame that is a 2-dimensional structure, resembling the 2-D arrays. Here, the input data is framed in the form of rows and columns. • Note: The size of the DataFrame Data Structure in Pandas is mutable.
- 8. DataFrame Example • Atwo-dimensional labeled data structure with columns of potentially different types. Output
- 9. Panel • Python Pandasmodule offers a Panel that is a 3-dimensional data structure and contains 3 axes to serve the following functions: • items: (axis 0) Every item of it corresponds to a DataFrame in it. • major_axis: (axis 1) It corresponds to the rows of each DataFrame. • minor_axis: (axis 2) It corresponds to the columns of each DataFrame.
- 10. Panel Data Example •Athree-dimensional data structure designed for handling 3D data. •As of Pandas version 1.0.0, Panels are deprecated and users are encouraged to use multi-index DataFrames import numpy as np panel = pd.Panel(np.random.rand(2, 3, 4), items=['Item1', 'Item2'], major_axis=['A', 'B', 'C'], minor_axis=['X1', 'X2', 'X3', 'X4']) print(panel)
- 11. Installation There are variousways to install the Python Pandas module. One of the easiest ways is to install using Python package installer i.e. PIP.
- 12. Creating a DataFramefrom a dictionary
- 13. Adding Data usingAppend Function
- 14. Getting Shape andInformation of the Data
- 15. Getting Statistical Analysisof Data Statistical data analysis is a procedure of performing various statistical operations. It is a kind of quantitative research, which seeks to quantify the data. Quantitative data basically involves descriptive data, such as survey data and observational data.
- 16.
- 17. Dropping Rows fromData Rows can be dropped using the “drop” method by specifying the index
- 18.
- 19. Q What isa Pandas Series, and how is it different from a DataFrame?
- 20. Q Explain thesteps involved in installing Pandas?
- 21. Q How canyou get the shape, information, and statistical analysis of a DataFrame?