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Pandas for Data Wrangling: Core Skills for Data Scientists
Rating: 4.6 out of 5(14 ratings)
3,073 students

Pandas for Data Wrangling: Core Skills for Data Scientists

Master data analysis with Pandas and Python through hands-on projects and real-world case studies.
Created byEDUCBA Bridging the Gap
Last updated 4/2024
English

What you'll learn

  • Data manipulation techniques using libraries like pandas in Python.
  • Statistical analysis methods for exploring and understanding datasets.
  • Machine learning algorithms and their applications for predictive modeling.
  • Data visualization techniques to effectively communicate insights.
  • Programming skills in Python and R languages.
  • Proficiency in using libraries such as NumPy, Matplotlib, scikit-learn, and TensorFlow.
  • Hands-on experience through projects and case studies.
  • Practical application of learned concepts to real-world data science problems.

Explore related topics

Course content

5 sections • 128 lectures • 16h 15m total length

  • Introduction to Pandas with Python4:44

    Explore the pandas package in python for data wrangling and analysis. Install via Anaconda, launch Jupyter, and perform filtering, sorting, and aggregations.

  • Understanding Jupiter Environment3:51

    Learn the Jupiter environment, switch between edit and command modes, and master keyboard shortcuts to create, execute, and manage cells while importing pandas and running data tasks.

  • Reading the Data Set9:01
  • Series and Data Frame3:34
  • Operations in Data Set3:53

    Perform basic operations in a data frame, including string concatenation and type checks, and create a new computed column by combining occupation and gender.

  • More on Panda Functions6:25
  • Column Names and Operation9:02

    Learn to manage pandas column names: view data types, rename with dicts or data.columns, use in-place updates, rename via Python lists, and remove underscores with string replace.

  • Removing Columns and Rows2:51
  • Sorting Data Frame3:00
  • Filtering Data7:10
  • Filter Multiple Criteria5:54
  • Selective Columns and Rows2:52
  • Data Frame and Series2:09
  • Axis Parameter2:53
  • String Methods in Pandas3:51

    Explore string methods in pandas, apply them via the str accessor to series, chain methods like upper and replace, and filter titles containing a word.

  • Changing the Data Types4:05
  • Example of Data Type Change4:27
  • Group by Functions4:06
  • Functions on Series4:20
  • Plotting series in Pandas3:40
  • Dealing with Null Values4:44
  • Uses of Index6:06
  • Column in Index5:51
  • Output of Data8:20
  • Functions of iX Method8:39

    Learn how the ix method mixes labels and positions to select values in a drinks-by-country dataset, with slicing ideas and a caution about potential confusion.

  • InPlace Parameter4:29

    Learn how the inplace parameter affects dropping the continent column in a pandas dataframe, why inplace defaults to false, and how to drop permanently by assignment.

  • Inspecting the Space4:49
  • Reducing the Space9:46

    Learn how to reduce a data frame's memory footprint in pandas by converting the continent column to a category, using codes, and relying on implicit lookup for efficient queries.

  • Using in Country Series4:10
  • Creating Manual Data Frame6:50
  • Random Sampling with Pandas3:42
  • Concept of Dummy Coding11:31
  • Creating Dummified Values4:15
  • Duplicates in Data Frame8:50

    Learn to detect and count duplicates in a pandas data frame using the duplicated and drop_duplicates functions, exploring first versus last occurrence and in-place options.

  • Functions for Date and Time9:10

    Learn to convert string time data to pandas datetime, use dt accessors for time-based operations, and filter data by date ranges, with examples on a UFO dataset.

  • Setting with Copy Warning4:56

    Avoid the pandas setting with copy warning by using the lock method when updating a subset and convert zero beer_servings to NaN with numpy.

  • Example on Copy Warning4:41

    Learn how to handle copy warnings in pandas by creating an explicit copy with df.copy(), using loc to replace values with NaN, and clarifying view vs copy for readable code.

  • Changing the Display Option4:29

    Change pandas display settings to control row output, using get_option and set_option to adjust display.max_rows (e.g., 200 or none) and reset_option to return to defaults.

  • Formatting the Data6:57
  • Tricks for Display Options7:10

    Explore pandas display options, search methods by keyword, reset session options, and construct data frames from dictionaries, lists, and numpy arrays to enhance data wrangling workflows.

  • Data with Rows and Columns9:10
  • Converting Data Frame4:19
  • Introduction to Azure Data Lake7:24
  • Merging Data Frames8:32
  • Shaping a Data Frame7:41
  • Filling NA Values2:41
  • Importing Time Series Data7:37

    Import a time series temperature dataset from csv, identify nan values, and apply forward fill or backfill strategies with optional limit and access parameters to contextually fill missing data.

  • Working with Interpolate Method9:38
  • Stacking and Unstacking10:05

    Master stacking and unstacking in pandas to move between rows and columns, with practical Excel data; learn level-based headers, stack operations, and unstack to reshape data frames.

  • Stacking and Unstacking for 3 Levels3:51
  • Concept of Crosstab8:50
  • More on Crosstab7:39

    Learn to use cross tab on repeating columns, test distributions, and draw insights from embarked, sex, fare, and cabin to understand survival patterns.

  • More Options with Crosstab9:37
  • Functions of Pivot3:52
  • Pivot Table Method6:29

    Discover how the pivot_table method creates spreadsheet-style pivots, handling duplicate day and city entries, with configurable aggregates like mean or max and optional margins.

  • Example on Pivot Table9:46
  • Data Frame to CSV File6:30

    Learn to write a data frame to a csv file, including replacing values, selecting columns, and controlling index and header options for clean output.

  • Using Excel Functions7:31
  • Summary on Pandas2:37

Requirements

  • Students should have a basic understanding of programming concepts, preferably in Python, and a fundamental grasp of mathematics and statistics.

Description

Welcome to the "Data Analysis with Pandas and Python" course! This course is designed to equip you with the essential skills and knowledge required to proficiently analyze and manipulate data using the powerful Pandas library in Python.

Whether you're a beginner or have some experience with Python programming, this course will provide you with a solid foundation in data analysis techniques and tools. Throughout the course, you'll learn how to read, clean, transform, and analyze data efficiently using Pandas, one of the most widely used libraries for data manipulation in Python.

From understanding the basics of Pandas data structures like Series and DataFrames to performing advanced operations such as grouping, filtering, and plotting data, each section of this course is crafted to progressively enhance your proficiency in data analysis.

Moreover, you'll have the opportunity to apply your skills in real-world scenarios through case studies and projects, allowing you to gain hands-on experience and build a portfolio of projects to showcase your expertise.

By the end of this course, you'll have the confidence and competence to tackle a wide range of data analysis tasks using Pandas and Python, empowering you to extract valuable insights and make informed decisions from diverse datasets. Let's embark on this exciting journey into the world of data analysis together!

Section 1: Pandas with Python Tutorial

In this section, students will embark on a comprehensive journey into using Pandas with Python for data manipulation and analysis. Starting with an introductory lecture, they will become familiar with the Pandas library and its integration within the Python ecosystem. Subsequent lectures will cover practical aspects such as reading datasets, understanding data structures like Series and DataFrames, performing operations on datasets, filtering and sorting data, and dealing with missing values. Advanced topics include manipulating string data, changing data types, grouping data, and plotting data using Pandas.

Section 2: NumPy and Pandas Python

The following section introduces students to NumPy, a fundamental package for scientific computing in Python, and its integration with Pandas. After an initial introduction to NumPy, students will learn about the advantages of using NumPy over traditional Python lists for numerical operations. They will explore various NumPy functions for creating arrays, performing basic operations, and slicing and dicing arrays. The section then seamlessly transitions to Pandas, where students will learn to create DataFrames from Series and dictionaries, perform data manipulation operations, and generate summary statistics on data.

Section 3: Data Analysis With Pandas And Python

This section focuses on practical data analysis using Pandas and Python. Students will learn about the installation of necessary software, downloading and loading datasets, and slicing and dicing data for analysis. A case study involving the analysis of retail dataset management will allow students to apply their newfound skills in a real-world scenario, gaining valuable experience in data management and analysis tasks.

Section 4: Pandas Python Case Study - Data Management for Retail Dataset

In this section, students will delve deeper into a comprehensive case study involving the management of a retail dataset using Pandas. They will work through various parts of the project, including data cleaning, transformation, and analysis, gaining hands-on experience in handling large datasets and deriving actionable insights from them.

Section 5: Analyzing the Quality of White Wines using NumPy Python

The final section introduces students to a specific application of data analysis using NumPy and Python: analyzing the quality of white wines. Through file handling, slicing, sorting, and gradient descent techniques, students will learn how to analyze and draw conclusions from real-world datasets, reinforcing their understanding of NumPy and Python for data analysis tasks.

Who this course is for:

  • Aspiring data scientists, analysts, researchers, and anyone interested in data science careers.
  • Individuals with a passion for data analysis and a desire to acquire essential skills in data science.
  • Students seeking to enhance their knowledge and proficiency in data manipulation, visualization, and analysis.
  • Professionals aiming to transition into data-related roles or advance their careers in data science.
  • Anyone looking to develop practical skills in statistical analysis, machine learning, and data-driven decision-making.

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