Data corner: Pandas (data cleaning, preprocessing, data exploration, analysis, data transformation, time series analysis, data visualization)

This article will be a brief overview of 5 very important skills someone dealing with pandas will have to have. The areas that will be covered are:

1. Data Cleaning and Preprocessing
2. Data Exploration and Analysis
3. Data Transformation
4. Time Series Analysis
5. Data Visualization

Each section includes an explanation of what this topic is, when it would be useful in an internship, and two videos about it. (One video is brief while the other is longer.)

Skill #1: Data Cleaning and Preprocessing

Overview:

• This involves preparing your data for analysis by handling missing values, removing duplicates, and replacing or transforming data as needed. Pandas provides functions like dropna(), drop_duplicates(), and replace() to facilitate these tasks.

Example Task:

• Clean a dataset by identifying and handling missing values, and then removing duplicate entries to ensure the data is ready for further analysis.

Resources: 

1. How to Clean Data with Python Pandas (Video, ~6 minutes)

If you want a way more in-depth look into what you can do:

• Cleaning Data with Python and Pandas (Video, ~15 minutes)

Skill #2: Data Exploration and Analysis

Overview:

• Pandas helps you explore and analyze your dataset by offering functions like head() for a quick preview, info() for essential information, and describe() for statistical summaries. Operations like groupby() and filtering allow for in-depth analysis.

Example Task:

• Conduct exploratory data analysis (EDA) on a given dataset, generating summary statistics and visualizations to uncover patterns and trends.

Resources: 

1. Basic Ways Pandas is Used for Exploring Data (Video, ~2 minutes)

If you want a way more in-depth look into what you can do:

• Deep Dive into Using Pandas for Exploring Data (Video, ~30 minutes)

Skill #3: Data Transformation

Overview:

• This involves reshaping, merging, or combining datasets to suit your analysis needs. Pandas provides functions like merge(), concat(), and tools like pivot_table() and melt() for these transformation tasks.

Example Task:

• Merge two datasets based on a common column, creating a consolidated dataset for further analysis.

Resources: 

1. How to Merge Data Frames (Video, ~2 minutes)

If you want a way more in-depth look into what you can do:

• How to Create a Great Pivot Table (Video, ~9 minutes)

Skill #4: Time Series Analysis

Overview:

• Pandas excels in handling time series data, providing tools for date and time manipulation, along with functions like resample() and rolling() for time-based grouping and calculations. Time-based indexing simplifies working with temporal data.

Example Task:

• Analyze the monthly trends in sales data, using pandas to resample and visualize the sales over time.

Resources: 

1. Time-Series Analysis/Pandas (Video, ~7 minutes)

If you want a way more in-depth look into what you can do:

• Here is a much more in depth series of videos about time series analysis with pandas. It includes 7 videos each focusing on different parts of time series data. This is the first of the 7 videos: (each about 10 minutes)

Skill #5: Data Visualization:

Overview:

• While pandas itself is not a visualization library, it seamlessly integrates with Matplotlib and Seaborn. You can leverage pandas DataFrames to prepare data for visualization, making it easier to create plots and charts using these visualization libraries.

Example Task:

• Create visualizations of a dataset’s key metrics using Matplotlib and pandas, presenting insights through clear and informative charts.

Resources: 

1. Plotting with Pandas DataFrames (Video, ~2 minutes)

If you want a way more in-depth look into what you can do:

• MatPlotLib and Pandas to Create Data Visualizations (Video, ~22 minutes)