Branch-AI/one_md_per_day_format/piscine/Week1/day3.md

D03 Piscine AI - Data Science

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Introduction

While working on a dataset it is important to check the distribution of the data. Obviously, for most of humans it is difficult to visualize the data in more than 3 dimensions

Viz is important to understand the data and to show results. We have already seen there are some basinc viz functionalities in Pandas. Now we'll discover two of the most know viz libraries in Python:

• Pandas viz
• Matplotlib
• Plotly

Pandas viz is pratique: rapid plot, relies on Matplotlib. (check matplotlib doc sometimes not all params are detailed in pandas doc) For more elaborate plots Matplotlib is necessary

And finaly Plotly is a interactive plot library.

Rules

Always a title, legend, ...

Ressources

https://matplotlib.org/3.3.3/tutorials/index.html https://towardsdatascience.com/matplotlib-tutorial-learn-basics-of-pythons-powerful-plotting-library-b5d1b8f67596

https://github.com/rougier/matplotlib-tutorial https://jakevdp.github.io/PythonDataScienceHandbook/05.13-kernel-density-estimation.html

Exercise 1 Pandas plot 1

The goal of this exercise is to learn to create plots with use Pandas. Panda's .plot() is a wrapper for matplotlib.pyplot.plot().

Here is the data we will be using:

        df = pd.DataFrame({
        'name':['christopher','marion','maria','mia','clement','randy','remi'],
        'age':[70,30,22,19,45,33,20],
        'gender':['M','F','F','F','M','M','M'],
        'state':['california','dc','california','dc','california','new york','porto'],
        'num_children':[2,0,0,3,8,1,4],
        'num_pets':[5,1,0,5,2,2,3]
        })

1. Reproduce this plot. This plot is called a bar plot

The plot has to contain:

• the title
• name on x-axis
• legend

Correction

1. This question is validated if the plot reproduces the plot in the image. It has to contain a title, an x-axis name and a legend.

Exercise 2: Pandas plot 2

The goal of this exercise is to learn to create plots with use Pandas. Panda's .plot() is a wrapper for matplotlib.pyplot.plot().

        df = pd.DataFrame({
        'name':['christopher','marion','maria','mia','clement','randy','remi'],
        'age':[70,30,22,19,45,33,20],
        'gender':['M','F','F','F','M','M','M'],
        'state':['california','dc','california','dc','california','new york','porto'],
        'num_children':[2,0,0,3,8,1,4],
        'num_pets':[5,1,0,5,2,2,3]
        })

1. Reproduce this plot. This plot is called a scatter plot. Do you observe a relationship between the age and the number of children ?

The plot has to contain:

• the title
• name on x-axis
• name on y-axis

Correction

1. This question is validated if the plot reproduces the plot in the image. It has to contain a title, an x-axis name and an y-axis name. You should also observe that the older people are the bigger the number of children is.

Exercise 3 Matplotlib 1

The goal of this plot is to learn to use Matplotlib to plot data. As you know, Matplotlib is the underlying library used by Pandas. It provides more options to plot custom visualizations. Howerver, most of the plots we will create with Matplotlib can be reproduced with Pandas' .plot().

1. Reproduce this plot. We assume the datapoints have integers coordinates.

The plot has to contain:

• the title

• name on x-axis and y-axis

• x-axis and y-axis are limited to [1,8]

• style:

    - red dashdot line with a width of 3
    - blue circles with a size of 12
  

Correction

1. This question is validated if the plot reproduces the plot in the image and respect those criterias

    - the title
    - name on x-axis and y-axis
    - x-axis and y-axis are limited to [1,8]
    - **style**: 
   
                    - red dashdot line with a width of 3
                    - blue circles with a size of 12
   

Exercise 4 Matplotlib 2

The goal of this plot is to learn to use Matplotlib to plot different lines in the same plot on different axis using twinx. This very useful to compare variables in different ranges.

Here is the data:

left_data = [5, 7, 11, 13, 17]
right_data = [0.1, 0.2, 0.4, 0.8, -1.6]
x_axis = [0.0, 1.0, 2.0, 3.0, 4.0]

1. Reproduce this plot

The plot has to contain:

• the title

• name on left y-axis and right y-axis

• style:

    - left data in black
    - right data in red
  

Correction

1. This question is validated if the plot reproduces the plot in the image and respect those criterias

    The plot has to contain: 
   
    - the title
    - name on left y-axis and right y-axis
    - **style**: 
   
            - left data in black
            - right data in red
   

https://matplotlib.org/gallery/api/two_scales.html

Exercise 5 Matplotlib subplots

The goal of this exerice is to learn to use Matplotlib to create subplots.

1. Reproduce this plot using a for loop:

The plot has to contain:

• 6 subplots: 2 rows, 3 columns

• Keep space between plots: hspace=0.5 and wspace=0.5

• Each plot contains

  • Text (2,3,i) centered at 0.5, 0.5. Hint: check the parameter ha of text
  • a title: Title i

Correction

1. The question is validated if the plot reproduces the image and the given criterias:

The plot has to contain:

• 6 subplots: 2 rows, 3 columns

• Keep space between plots: hspace=0.5 and wspace=0.5

• Each plot contains

  • Text (2,3,i) centered at 0.5, 0.5. Hint: check the parameter ha of text
  • a title: Title i

Check that the plot has been created with a for loop.

Exercise 6 Plotly 1

Plotly has evolved a lot in the previous years. It is important to always check the documentation.

Plotly comes with a high level interface: Plotly Express. It helps building some complex plots easily. The lesson won't detail the complex examples. Plotly express is quite interesting while using Pandas Dataframes because there are some built-in functions that leverage Pandas Dataframes.

The plot outputed by Plotly is interactive and can also be dynamic.

The goal of the exercise is to plot the price of a company. Its price is generated below.

returns = np.random.randn(50)
price = 100 + np.cumsum(returns)

dates = pd.date_range(start='2020-09-01', periods=50, freq='B')
df = pd.DataFrame(zip(dates, price),
                  columns=['Date','Company_A'])

1. Using Plotly express, reproduce the plot in the image. As the data is generated randomly I do not expect you to reproduce the same line.

The plot has to contain:

• title
• x-axis name
• yaxis name

2. Same question but now using plotly.graph_objects. You may need to use init_notebook_mode from plotly.offline.

https://plotly.com/python/time-series/e

Correction

1. This question is validated if the plot is in the image is reproduced using Plotly express given those criterias:

The plot has to contain:

• a title
• x-axis name
• yaxis name

2. This question is validated if the plot is in the image is reproduced using plotly.graph_objects given those criterias:

The plot has to contain:

• a title
• x-axis name
• yaxis name

Exercise 7 Plotly Box plots

The goal of this exercise is to learn to use Plotly to plot Box Plots. It is t is a method for graphically depicting groups of numerical data through their quartiles and values as min, max. It allows to compare quickly some variables.

Let us generate 3 random arrays from a normal distribution. And for each array add respectively 1, 2 to the normal distribution.

y0 = np.random.randn(50)
y1 = np.random.randn(50) + 1 # shift mean
y2 = np.random.randn(50) + 2

1. Plot in the same Figure 2 box plots as shown in the image. In this exercise the style is not important.

The plot has to contain:

• the title
• the legend https://plotly.com/python/box-plots/

Correction

1. This question is validated if the plot is in the image is reproduced given those criterias:

The plot has to contain:

• the title
• the legend

    ```
    import plotly.graph_objects as go
    import numpy as np

    y0 = np.random.randn(50)
    y1 = np.random.randn(50) + 1 # shift mean
    y2 = np.random.randn(50) + 2

    fig = go.Figure()
    fig.add_trace(go.Box(y=y0, name='Sample A',
                    marker_color = 'indianred'))
    fig.add_trace(go.Box(y=y1, name = 'Sample B',
                    marker_color = 'lightseagreen'))

    fig.show()
    ```