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Visualizations

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'll discover three libraries to visualize data in Python. These are one of the most used visualisation "libraries" in Python:

Pandas visualization module
Matplotlib
Plotly

The goal is to understand the basics of those libraries. You'll have time during the project to master one (or the three) of them. You may wonder why using one library is not enough. The reason is simple: it depends on the usage. For example if you want to check the data quickly you may want to use Pandas viz module or Matplotlib. If you want to plot a custom and more elaborated plot I suggest to use Matplotlib or Plotly. And, if you want to create a very nice and interactive plot I suggest to use Plotly.

Exercises of the day

Exercice 0: Environment and libraries
Exercise 1: Pandas plot 1
Exercise 2: Pandas plot 2
Exercise 3: Matplotlib 1
Exercise 4: Matplotlib 2
Exercise 5: Matplotlib subplots
Exercise 6: Plotly 1
Exercise 7: Plotly Box plots

Virtual Environment

Python 3.x
NumPy
Pandas
Matplotlib
Plotly
Jupyter or JupyterLab

I suggest to use the most recent version of the packages.

Resources

Exercise 0: Environment and libraries

The goal of this exercise is to set up the Python work environment with the required libraries.

Note: For each quest, your first exercice will be to set up the virtual environment with the required libraries.

I recommend to use:

the last stable versions of Python.
the virtual environment you're the most confortable with. virtualenv and conda are the most used in Data Science.
one of the most recents versions of the libraries required

Create a virtual environment named ex00, with a version of Python >= 3.8, with the following libraries: pandas, numpy, jupyter, matplotlib and plotly.

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]
        })

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

The plot has to contain:

the title
name on x-axis
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':[4,2,1,0,3,1,0],
        'num_pets':[5,1,0,2,2,2,3]
        })

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

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().

Reproduce this plot. We assume the data points 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

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]

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

Exercise 5: Matplotlib subplots

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

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

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'])

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
y-axis name

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

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

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/

7.6 KiB Raw Blame History

Visualizations

Exercises of the day

Virtual Environment

Resources

Exercise 0: Environment and libraries

Exercise 1: Pandas plot 1

Exercise 2: Pandas plot 2

Exercise 3: Matplotlib 1

Exercise 4: Matplotlib 2

Exercise 5: Matplotlib subplots

Exercise 6: Plotly 1

Exercise 7: Plotly Box plots

7.6 KiB

Raw Blame History