The goal of this day is to understand practical usage of **Pandas**.
As **Pandas** in intensively used in Data Science, other days of the piscine will be dedicated to it.
Not only is the **Pandas** library a central component of the data science toolkit but it is used in conjunction with other libraries in that collection.
**Pandas** is built on top of the NumPy package, meaning a lot of the structure of NumPy is used or replicated in **Pandas**. Data in **Pandas** is often used to feed statistical analysis in SciPy, plotting functions from Matplotlib, and machine learning algorithms in Scikit-learn.
Most of the topics we will cover today are explained and describes with examples in the first resource. The number of exercises is low on purpose: Take the time to understand the chapter 5 of the resource, even if there are 40 pages.
### Exercises of the day
- Exercice 0: Environment and libraries
- Exercise 1: Your first DataFrame
- Exercise 2: Electric power consumption
- Exercise 3: E-commerce purchases
- Exercise 4: Handling missing values
### Virtual Environment
- Python 3.x
- NumPy
- Pandas
- Jupyter or JupyterLab
_Version of Pandas I used to do the exercises: 1.0.1_.
I suggest to use the most recent one.
### Resources
- If I had to give you one resource it would be this one:
The goal of this exercise is to learn to manipulate real data with Pandas.
The data set used is [**Individual household electric power consumption**](https://assets.01-edu.org/ai-branch/piscine-ai/household_power_consumption.txt)
1. Delete the columns `Time`, `Sub_metering_2` and `Sub_metering_3`
2. Set `Date` as index
3. Create a function that takes as input the DataFrame with the data set and returns a DataFrame with updated types:
```python
def update_types(df):
#TODO
return df
```
4. Use `describe` to have an overview on the data set
5. Delete the rows with missing values
6. Modify `Sub_metering_1` by adding 1 to it and multiplying the total by 0.06. If x is a row the output is: (x+1)\*0.06
7. Select all the rows for which the Date is greater or equal than 2008-12-27 and `Voltage` is greater or equal than 242
8. Print the 88888th row.
9. What is the date for which the `Global_active_power` is maximal ?
10. Sort the first three columns by descending order of `Global_active_power` and ascending order of `Voltage`.
11. Compute the daily average of `Global_active_power`.
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# Exercise 3: E-commerce purchases
The goal of this exercise is to learn to manipulate real data with Pandas. This exercise is less guided since the exercise 2 should have given you a nice introduction.
3. What were the highest and lowest purchase prices?
4. How many people have English `'en'` as their Language of choice on the website?
5. How many people have the job title of `"Lawyer"` ?
6. How many people made the purchase during the `AM` and how many people made the purchase during `PM` ?
7. What are the 5 most common Job Titles?
8. Someone made a purchase that came from Lot: `"90 WT"` , what was the Purchase Price for this transaction?
9. What is the email of the person with the following Credit Card Number: `4926535242672853`
10. How many people have American Express as their Credit Card Provider and made a purchase above `$95` ?
11. How many people have a credit card that expires in `2025`?
12. What are the top 5 most popular email providers/hosts (e.g. gmail.com, yahoo.com, etc...)
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# Exercise 4: Handling missing values
The goal of this exercise is to learn to handle missing values. In the previous exercise we used the first techniques: filter out the missing values. We were lucky because the proportion of missing values was low. But in some cases, dropping the missing values is not possible because the filtered data set would be too small.
This article explains the different types of missing data and how they should be handled.
"**It’s important to understand these different types of missing data from a statistics point of view. The type of missing data will influence how you deal with filling in the missing values.**"
- Preliminary: Drop the `flower` column
1. Fill the missing values with a different "strategy" for each column:
`sepal_length` -> `mean`
`sepal_width` -> `median`
`petal_length`, `petal_width` -> `0`
2. Fill the missing values using the median of the associated column using `fillna`.
- Bonus questions:
- Filling the missing values by 0 or the mean of the associated column is common in Data Science. In that case, explain why filling the missing values with 0 or the mean is a bad idea.
