pandas for series and dataframe.pptx

pandas

1. Pandas in Series and Data frame Dr.R.SUNDAR CSE DEPT MITS

2. pandas in Series and data frame In pandas, the primary data structure used to store and manipulate data is called a " DataFrame ." A DataFrame is a two-dimensional , tabular data structure with labeled axes ( rows and columns ). It is similar to a spreadsheet or a SQL table. Each column in a DataFrame can have a different data type, and you can perform various operations like filtering, grouping, aggregation, and more on the data stored in a DataFrame . Pandas also provides another data structure called a " Series ," which is essentially a one-dimensional array-like object but with an associated index . Series can be thought of as a single column of a DataFrame . These data structures, DataFrame and Series , are powerful tools for data manipulation and analysis in Python, and they are an essential part of the pandas library.

3. Python Pandas Series 1.import pandas as pd # a simple char list list = ['g', 'e', 'e', 'k', 's ']   # create series form a char list res = pd.Series (list) print(res ) 2.import pandas as pd # a simple int list list = [1,2,3,4,5 ] # create series form a int list res = pd.Series (list) print(res)

4. pandas in Series and data frame 3.import pandas as pd dic = { 'Id': 1013, 'Name': ' MOhe ',      'State': ' Maniput ','Age': 24 } res = pd.Series ( dic ) print(res ) 4.import pandas as pd   # list of strings lst = [‘python', 'For', ‘series', ‘and',       ‘ D ataframe ', ‘is', ‘object'] # Calling DataFrame constructor on list df = pd.DataFrame ( lst ) display( df )

5. pandas in Series and data frame import pandas as pd # initialise data of lists. data = {'Name':['Tom', 'nick', ' krish ', 'jack'],     'Age':[20, 21, 19, 18 ]}   # Create DataFrame df = pd.DataFrame (data ) d isplay( df )

6. pandas in Series and data frame import pandas as pd # Define a dictionary containing employee data data = {'Name':['Jai', ' Princi ', 'Gaurav', ' Anuj '],         'Age':[27, 24, 22, 32],         'Address':['Delhi', 'Kanpur', 'Allahabad', ' Kannauj '],         'Qualification':[' Msc ', 'MA', 'MCA', ' Phd ']}   # Convert the dictionary into DataFrame df = pd.DataFrame (data ) # select two columns print( df [['Name', 'Qualification']])

7. pandas in Series and data frame from pandas import DataFrame # Creating a data frame Data = {'Name': [' Mohe ', ' Shyni ', ' Parul ', 'Sam'],         'ID': [12, 43, 54, 32],         'Place': ['Delhi', 'Kochi', 'Pune', 'Patna ']     df = DataFrame (Data, columns = ['Name', 'ID', 'Place ']) # Print original data frame print("Original data frame:\n") display( df ) # Selecting the product of Electronic Type select_prod = df.loc [ df ['Name'] == ' Mohe '] print("\n ")     # Print selected rows based on the condition print("Selecting rows:\n") display ( select_prod )

8. pandas in Series and data frame from pandas import DataFrame # Creating a data frame Data = {'Name': [' Mohe ', ' Shyni ', ' Parul ', 'Sam'],         'ID': [12, 43, 54, 32],         'Place': ['Delhi', 'Kochi', 'Pune', 'Patna']         }   df = DataFrame (Data, columns = ['Name', 'ID', 'Place ']) # Print original data frame print("Original data frame:") display( df ) print("Selected column: ") display( df [['Name', 'ID']] )

9. Indexing & Selecting & Filtering in Pandas import numpy as np import pandas as pd obj = pd.Series ( np.arange (5),index=[' a','b','c','d','f ']) obj Working with Index in Series obj ['c '] entering the index number in square brackets. obj [2 ] slice the data. Obj [0:3] Selecting in Series select the specific rows . Obj [[‘ a’,’c ’]] Selecting the index number . Obj [[0,2]]

10. Filtering in Series Obj [ obj <2]---------values less than 2. Slice the values Obj [‘ a’:’c ’] Assign a value of sliced piece. Obj [‘ b’:’c ’]=5 obj

11. Selecting in DataFrame how to index in DataFrame , let me create a DataFrame . data= pd.DataFrame ( np.arange (16).reshape(4,4 ), index =[' London','Paris','Berlin','India '], columns =[' one','two','three','four ']) Data The column named two data['two'] select more than one column data[[' one','two ']] slice the rows . data[:3]

12. Filtering in DataFrame data[data['four']>5] Assign data to specific values . d ata[data<5]=0 data Selecting with iloc and loc methods The iloc method to select a row using the row’s index . data.iloc [1 ] select the specific columns of the row d ata.iloc [1,[1,2,3]] select specific columns of multiple rows data.iloc [[1,3],[1,2,3]]

13. Filtering in DataFrame loc method need to use names for loc . data.loc ['Paris',[' one','two ']] data.loc [:' Paris','four ']