https://github.com/mamtapanda088/dataanalaysis-warmup-
Tasks: Create a DataFrame: Convert the dictionary into a pandas DataFrame. Top and Bottom Rows: Display the top 3 bottom ,3 rows of the DataFrame. Summary Statistics: Generate summary statistics for the dataset. Gender Count: Count the occurrences of each gender. Marks Analysis: Calculate the average, maxi, and min marks. Tools Used: Python ,pandas
https://github.com/mamtapanda088/dataanalaysis-warmup-
data-analysis data-science jupyter-notebook visualization
Last synced: over 1 year ago
JSON representation
Tasks: Create a DataFrame: Convert the dictionary into a pandas DataFrame. Top and Bottom Rows: Display the top 3 bottom ,3 rows of the DataFrame. Summary Statistics: Generate summary statistics for the dataset. Gender Count: Count the occurrences of each gender. Marks Analysis: Calculate the average, maxi, and min marks. Tools Used: Python ,pandas
- Host: GitHub
- URL: https://github.com/mamtapanda088/dataanalaysis-warmup-
- Owner: mamtapanda088
- Created: 2024-08-15T13:03:53.000Z (almost 2 years ago)
- Default Branch: main
- Last Pushed: 2024-08-15T13:14:14.000Z (almost 2 years ago)
- Last Synced: 2025-02-09T13:17:08.370Z (over 1 year ago)
- Topics: data-analysis, data-science, jupyter-notebook, visualization
- Language: Jupyter Notebook
- Homepage: https://github.com/mamtapanda088/Dataanalaysis-warmup-
- Size: 5.86 KB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
Awesome Lists containing this project
README
Explanation:
Creating the Dataset: The dictionary dict1 is defined with keys ‘Name’, ‘Marks’, and ‘Gender’.
Creating a DataFrame: The pandas DataFrame is created from the dictionary.
Top 3 Rows: The head(3) method is used to display the top 3 rows of the DataFrame.
Bottom 3 Rows: The tail(3) method is used to display the bottom 3 rows of the DataFrame.
Summary Statistics: The describe(include='all') method provides summary statistics for all columns, including count, unique values, top values, frequency, mean, standard deviation, min, max, and quartiles.
Gender Count: The value_counts() method is used to count the occurrences of each gender.
Average Marks: The mean() method calculates the average marks.
Maximum and Minimum Marks: The max() and min() methods find the highest and lowest marks, respectively.