Pandas Data Analysis Notes

Overview of Data Analysis with Pandas

• The lecturer emphasizes the learning of data analysis using the Pandas library in Python.
• The focus is on using specific datasets related to speed skaters from different years and athlete information.

Data Preparation Tasks

• Creation of Jupyter Notebook:
• Start by creating a new Jupyter Notebook.
• Importing Pandas:
• Ensure to import the Pandas library at the beginning of your notebook using the command:

  import pandas as pd

• Loading CSV Files:
• Load the multiple CSV files that contain race results and athlete data.
• Example of reading a file:

  races_2021 = pd.read_csv('races_2021.csv')

Concatenating DataFrames

• Combine the race data from different years using the pd.concat() method:
• Example:

  all_races = pd.concat([races_2020, races_2021, races_2022, races_2023])

Merging DataFrames

• After concatenating the race files, merge this consolidated data with athlete data using the pd.merge() function.
• Specify the columns that are common to both DataFrames to perform the merge.
• Example:

  merged_data = pd.merge(all_races, athlete_data, on='athlete_id')

Data Analysis Techniques

• Use Boolean indexing to filter and analyze the data based on specific conditions.
• Example:

  fast_skaters = merged_data[merged_data['speed'] >= 30]

• Group By and Apply:
• Use groupby() function in Pandas to group data based on a certain column and apply functions to analyze data within those groups.
• Example:

  average_speed_by_year = merged_data.groupby('year')['speed'].mean()

Markdown Cells for Explanations

• It is important to use Markdown cells to document and explain the steps taken in your analysis.
• This facilitates understanding for anyone reviewing your work.

Collaboration and Assistance

• Work in groups of two or three to facilitate collaboration and peer learning.
• If assistance is needed, reach out to the Teaching Assistants (TAs) present during the session for guidance.

Review of Lecture Videos

• Revisit previous lecture videos related to data handling and analysis in Pandas for clarity and additional insights as needed.

Final Reminders

• Ensure all necessary files are in the same folder to avoid file path errors.
• Completion of the exercise may require handling missing values using techniques such as fillna().
• Stay focused on your individual analysis approach to develop unique insights from the dataset.