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Data Analysis with Python: Zero to Pandas - Course Project Guidelines

(remove this cell before submission)

Make submissions here:

This is the starter notebook for the course project for Data Analysis with Python: Zero to Pandas. For the course project, you will pick a real-world dataset of your choice and apply the concepts learned in this course to perform exploratory data analysis. Use this starter notebook as an outline for your project (you can also start with an empty new notebook). Focus on documentation and presentation - this Jupyter notebook will also serve as a project report, so make sure to include detailed explanations whererver possible using Markdown cells.

Step 1: Select a real-world dataset
  • Find and download an interesting real-world dataset (see the Recommended Datasets section below for ideas).

  • The dataset should contain tabular data (rowsn & columns), preferably in CSV/JSON/XLS or other formats that can be read using Pandas. If it's not in a compatible format, you may have to write some code to convert it to a desired format.

  • The dataset should contain at least 3 columns and 150 rows of data. You can also combine data from multiple sources to create a large enough dataset.

Step 2: Perform data preparation & cleaning
  • Load the dataset into a data frame using Pandas
  • Explore the number of rows & columns, ranges of values etc.
  • Handle missing, incorrect and invalid data
  • Perform any additional steps (parsing dates, creating additional columns, merging multiple dataset etc.)
Step 3: Perform exploratory Analysis & Visualization
  • Compute the mean, sum, range and other interesting statistics for numeric columns
  • Explore distributions of numeric columns using histograms etc.
  • Explore relationship between columns using scatter plots, bar charts etc.
  • Make a note of interesting insights from the exploratory analysis
Step 4: Ask & answer questions about the data
  • Ask at least 5 interesting questions about your dataset
  • Answer the questions either by computing the results using Numpy/Pandas or by plotting graphs using Matplotlib/Seaborn
  • Create new columns, merge multiple dataset and perform grouping/aggregation wherever necessary
  • Wherever you're using a library function from Pandas/Numpy/Matplotlib etc. explain briefly what it does
Step 5: Summarize your inferences & write a conclusion
  • Write a summary of what you've learned from the analysis
  • Include interesting insights and graphs from previous sections
  • Share ideas for future work on the same topic using other relevant datasets
  • Share links to resources you found useful during your analysis
Step 6: Make a submission & share your work
(Optional) Step 7: Write a blog post

Recommended Datasets

Use the following resources for finding interesting datasets:

Example Projects

Refer to these projects for inspiration:

Evaluation Criteria

Your submission will be evaluated using the following criteria:

  • Dataset must contain at least 3 columns and 150 rows of data
  • You must ask and answer at least 5 questions about the dataset
  • Your submission must include at least 5 visualizations (graphs)
  • Your submission must include explanations using markdown cells, apart from the code.
  • Your work must not be plagiarized i.e. copy-pasted for somewhere else.

NOTE: Remove this cell containing the instructions before making your submission. You can do using the "Edit > Delete Cells" menu option.

Project Title

Write some introduction about your project here: describe the dataset, where you got it from, what you're trying to do with it, and which tools & techniques you're using. You can also mention about the course, and what you've learned from it.

As a first step, let's upload our Jupyter notebook to

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project_name = "zerotopandas-course-project-Pokemon"
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!pip install jovian --upgrade -q
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import jovian
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Data Preparation and Cleaning


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import pandas as pd
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Pokemon = pd.read_csv('Pokemon.csv')
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(800, 13)
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Pokemon['Name'] = Pokemon['Name'].str.replace(".*(?=Mega)", "")
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Pokemon= Pokemon.set_index('Name')
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Exploratory Analysis and Visualization


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Number_of_Pokemon= Pokemon.shape[0]
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print('There are {} Pokemon in this dataset.'.format(Number_of_Pokemon))
There are 800 Pokemon in this dataset.
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Pokemon['Type 1'].unique()
array(['Grass', 'Fire', 'Water', 'Bug', 'Normal', 'Poison', 'Electric',
       'Ground', 'Fairy', 'Fighting', 'Psychic', 'Rock', 'Ghost', 'Ice',
       'Dragon', 'Dark', 'Steel', 'Flying'], dtype=object)
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array([1, 2, 3, 4, 5, 6], dtype=int64)
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Asking and Answering Questions


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Inferences and Conclusion


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References and Future Work


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[jovian] Attempting to save notebook..
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