DataFrames Visualization

There are numerous DataFrame plotting methods available. Throughout this section, we’ll use the following dataframes.

import pandas as pd

df_grades = pd.read_csv('https://raw.githubusercontent.com/datasmp/datasets/main/grades.csv')
df_grades.head()

	Name	ID	Grade	Gender	HW	Test-1	Test-2	Test-3	Test-4	Final
0	mqtvy	37047871	10	M	30	91	69	93	17	50
1	jbbsx	35439616	11	F	6	18	93	9	98	91
2	mrvab	35543247	11	M	78	92	60	43	34	26
3	bjyve	61282135	9	M	60	8	10	99	80	87
4	rlpsr	53448034	10	M	3	38	45	43	79	69

df_stock = pd.read_excel('https://raw.githubusercontent.com/datasmp/datasets/main/stock.xlsx')
df_stock.head()

	Date	APPLE	TESLA	AMAZON	VISA	SP500
0	2020-01-02	74.33	86.05	1898.01	189.66	3257.85
1	2020-01-03	73.61	88.60	1874.97	188.15	3234.85
2	2020-01-06	74.20	90.31	1902.88	187.74	3246.28
3	2020-01-07	73.85	93.81	1906.86	187.24	3237.18
4	2020-01-08	75.04	98.43	1891.97	190.45	3253.05

Plot Types

Line Plot

• The default value of the kind parameter is ‘line’.

# all columns
df_stock.plot();     # kind = 'line'

df_stock.plot( y='AMAZON');

df_stock.plot( y=['TESLA','APPLE']);     # two columns

Scatter Plot

• x-coordinates should be provided.

df_stock.plot(x='APPLE', y='AMAZON', kind='scatter');

Histogram

df_stock.plot(y='AMAZON', kind='hist');

# horizontal
df_stock.plot(y='AMAZON', kind='hist', orientation='horizontal');

df_stock.hist(layout=(1,5), figsize=(10,4));

Bar Plot

df_stock.head().plot(kind='bar');

df_stock.head().plot(kind='barh');

Stacked Bar

df_stock.head().plot(kind='bar', stacked=True);

df_stock.head().plot(kind='barh', stacked=True);

Pie Chart

df_grades.Gender.value_counts()

Gender
M    54
F    46
Name: count, dtype: int64

df_grades.Gender.value_counts().plot(kind='pie', autopct='%.0f%%' );

kde

df_stock.plot(kind='kde');

df_stock.plot(y='VISA', kind='kde');

Box Plot

df_stock.plot(kind='box');

df_stock.plot(y='VISA', kind='box');

# horizontal
df_stock.plot(y='VISA', kind='box', vert=False);

df_stock.boxplot();

Area

df_stock.plot(kind='area');

Plotting Parameters

Color

df_stock.plot( y='AMAZON', c='red'); 

Figsize

df_stock.plot( y='AMAZON', figsize=(20,4)); 

Title

df_stock.plot( y='AMAZON', title='Stock Prices'); 

Axis Labels

df_stock.plot( y='AMAZON', xlabel='Day', ylabel='Stock Values'); 

Linewidth

df_stock.plot( y='AMAZON', linewidth=5); 

Linestyle

df_stock.plot( y='AMAZON', linestyle='dashed'); 

df_stock.plot( y='AMAZON', linestyle='dotted'); 

Size

df_stock.plot(x='APPLE', y='AMAZON', kind='scatter', s=20);

Matplotlib and Dataframes

import matplotlib.pyplot as plt

In the following code:

• the index_col parameter sets the first column as the index of the DataFrame.

• The parse_dates parameter converts string index values into Timestamps so they are considered as dates.

df_stock = pd.read_excel('https://raw.githubusercontent.com/datasmp/datasets/main/stock.xlsx', index_col=0, parse_dates=True)
df_stock.head()

	APPLE	TESLA	AMAZON	VISA	SP500
Date
2020-01-02	74.33	86.05	1898.01	189.66	3257.85
2020-01-03	73.61	88.60	1874.97	188.15	3234.85
2020-01-06	74.20	90.31	1902.88	187.74	3246.28
2020-01-07	73.85	93.81	1906.86	187.24	3237.18
2020-01-08	75.04	98.43	1891.97	190.45	3253.05

Scatter Plot

• x-coordinates should be provided.

plt.figure(figsize=(20,4))
plt.scatter(df_stock.index, df_stock['APPLE'], c='r');

plt.figure(figsize=(10,4))
plt.scatter(df_stock['VISA'], df_stock['APPLE'], c='navy', marker='*');

Line Plot

• The default x values are the indexes of the DataFrame.

plt.figure(figsize=(20,4))
plt.plot(df_stock['APPLE'], c='r');

plt.figure(figsize=(20,4))
plt.plot(df_stock['VISA'], c='g', label='VISA')
plt.plot(df_stock['APPLE'], c='r', label='APPLE', linestyle='dotted')
plt.legend();

plt.figure(figsize=(20,5))
plt.axes().set_facecolor('black');
plt.plot(df_stock['VISA'], color='orange')
plt.grid(visible=False);

Histogram

plt.hist(df_stock['APPLE']);