The document outlines a plan for data manipulation, focusing on plotting and visualization using libraries like Matplotlib, Pandas, Seaborn, and Bokeh. It covers basic and customized plotting techniques, annotations, file handling, and the creation of various plots such as line, bar, and scatter plots. Additionally, it mentions interactive graphics with Plotly and provides references for further reading.

1. Data manipulation:
Plotting and Visualization
AAA-Python Edition

2. Plan
●
1- matplotlib
●
2- Plotting with Pandas and seaborn
●
3- Interactive and dynamic graphics

3. 3
1-matplotlib
[By Amina Delali]
Basic PlottingBasic Plotting
●
Importing pyplot module
from matplotlib (as plt)
ser1 The series values are the
y values, the series index
values, are the x values
You have to use this command, to
be able to see all the plots
( embed an image after each plot)

4. 4
1-matplotlib
[By Amina Delali]
Basic PlottingBasic Plotting
●
To plot in this part just use:
axis2.plot(ser1)
To divide the
region in 4
subplots just
use:
add_subplot(
2,2,”position”)

5. 5
1-matplotlib
[By Amina Delali]
Customizing plotsCustomizing plots
●
(From google colab help)
colors
Some line styles and
markers

6. 6
1-matplotlib
[By Amina Delali]
Basic PlottingBasic Plotting
●
ser1
The xticklabes: 4
xticks ==> 4
labels
Mark the x axis at the
positions: 0, 2 4 and 6
and labeled A, B, C
and D
The title

7. 7
1-matplotlib
[By Amina Delali]
AnnotationsAnnotations
●
Creating the
rectangle
Adding the
rectangle to the
plot

8. 8
1-matplotlib
[By Amina Delali]
AnnotationsAnnotations
●
The arrow is drawn
at the left of the text
The y position of the
text (3) is under the
y position of the
annotation (5) so by
defalut the arrow is
drawn at the botom
of the plot

9. 9
1-matplotlib
[By Amina Delali]
File handling and configurationFile handling and configuration
●
You can specify an
svg file as well
●
You can customize the default options of matplotlib plots, you can use
the rc method
Real size

10. 10
2-Plottingwith
pandasandseaborn
[By Amina Delali]
Line plots with pandasLine plots with pandas
●ser1 df1
Plotting only these
2 columns

11. 11
2-Plottingwith
pandasandseaborn
[By Amina Delali]
Bar plots with pandasBar plots with pandas
●
To each value correspond a bar. In df1
plot, the bars are grouped by column
For horizontal
bars
The 3 values in
one bar

12. 12
2-Plottingwith
pandasandseaborn
[By Amina Delali]
Bar plots with seabornBar plots with seaborn
●
Importing seaborn library
An other column cat added to df1 used to separate
data in categories. Since there is no val2 duplicates,
so for each val2 there is only one bar

13. 13
2-Plottingwith
pandasandseaborn
[By Amina Delali]
HistogramsHistograms
●

14. 14
2-Plottingwith
pandasandseaborn
[By Amina Delali]
Points (scatter) plot with seabornPoints (scatter) plot with seaborn
●
Will plot the data points and
a linear regression model fit
The line that tries to fit
to the data

15. 15
2-Plottingwith
pandasandseaborn
[By Amina Delali]
Categorical DataCategorical Data
●
df4
Category 1, dimension 2 (col)
Category 2, dimension 2 (col)
Category 1, dimension 1 (hue)
Category 2, dimension 1 (hue)

16. 16
3-Interactive
anddynamicgraphics
[By Amina Delali]
BokehBokeh
●
To install bokeh library
Hexadecimal
colors code
X values
y values

17. 17
3-Interactive
anddynamicgraphics
[By Amina Delali]
plotlyplotly
●
You have to define this function and call it in each cell containing a plot

18. 18
3-interactive
Anddynamicgraphics
[By Amina Delali]
Basic PlottingBasic Plotting
●
The generated scatter plot

19. References
●
Wes McKinney. Python for data analysis: Data wrangling
with Pandas, NumPy, and IPython. O’Reilly Media, Inc, 2018.

20. Thank
you!
FOR ALL YOUR TIME