MachineLearning_Pandas - fsjames/FeliciaTalksTech-Academy GitHub Wiki

Pandas

Pandas is a library used for computation with tabular data. It allows you to put mixed types of data into a single table.

In pandas, a vector (1 dimension) is called a Series and an array (2 dimensions) is called a DataFrame

Series E.g.

import pandas as pd

>>> d = {'a': 1, 'b': 2, 'c': 3}

>>> ser = pd.Series(data=d, index=['a', 'b', 'c'])

>>> ser

a   1
b   2
c   3

dtype: int64

DataFrame E.g.

import pandas as pd

>>> d = {'col1': [1, 2], 'col2': [3, 4]}

>>> df = pd.DataFrame(data=d)

>>> df

   col1  col2
0     1     3
1     2     4

You can use pandas read_csv() method to read data from a csv file and store the data into a dataframe

# importing pandas module 

>>> import pandas as pd 
  
# importing regex module

>>> import re
    
# making data frame 

>>> data = pd.read_csv("https://media.geeksforgeeks.org/wp-content/uploads/nba.csv") 

>>> print(data)

You can also perform statistical data analysis on a dataframe using the describe() method

# removing null values to avoid errors 

>>> data.dropna(inplace = True) 
  
# percentile list

>>> perc =[.20, .40, .60, .80]
  
# list of dtypes to include

>>> include =['object', 'float', 'int']
  
# calling describe method

>>> desc = data.describe(percentiles = perc, include = include)
  
# display

>>> desc

Source: (https://www.geeksforgeeks.org/python-pandas-dataframe-describe-method/)