Data visulization helps indentify patterns and trends in large datasets.

There are 3 broder categories of visualization.

• Univariate Visualization
• Bivariate Visualization
• Multivariate Visualization

Univariate Visualization

Univariate visualisation is about visualise single attribute. First we need to find the data type of an and then we can visualise them. There are 4 data types: categorical nominal, categorical ordinal, metric discrete and metric continoues. Based on the data type we can choose appropriate visualization

Consider the following dataset about t-shirts.

#Below we create the above dataset using random function. Let us generate a dataframe for 1000 randomly generate t-shirt information.
import random
import pandas as pd
import matplotlib
import matplotlib.pyplot as plt
import numpy as np
import string

import seaborn as sns

print('matplotlib: {}'. format(  matplotlib. __version__)) 

sizes = ["S", "M", "L", "XL","XXL"]
colors = ["black", "red", "white", "blue"]

#Below we create functions to generate a probable value for each column attribute randomly.
def generate_random_id():
    return str(random.randrange(100,999))+random.choice(string.ascii_uppercase)

def generate_random_size():
    return random.choices(sizes,weights=[0.15, 0.32, 0.28,0.20,0.05])[0]

def generate_random_color():
    return random.choice(colors)

def generate_random_price():
    return round(random.uniform(0, 10000),2)

def generate_random_stock():
    return random.randrange(12,2345)
    
total_no_of_tshirts=1000
data = []
for i in range(total_no_of_tshirts):
  row = []
  row.append(generate_random_id())
  row.append(generate_random_size())
  row.append(generate_random_color())
  row.append(generate_random_price())
  row.append(generate_random_stock())
  data.append(row)
df=pd.DataFrame(data,columns = ['id', 'size','color','price','stock'])
print(df)

Output,

matplotlib: 3.5.3
       id size  color    price  stock
0    885J    S    red  1004.74   1642
1    422W  XXL  black   629.46   2065
2    642L    M    red  2224.21   1789
3    231I    S    red  8137.09   1519
4    551D    S   blue  9303.51    132
..    ...  ...    ...      ...    ...
995  530W   XL  black  4792.29    948
996  921V    M  white  6985.76   1266
997  119Q    M    red  8022.32   1008
998  703T    M   blue   853.45   2338
999  233U   XL  white  6272.56   1924

[1000 rows x 5 columns]