Panda Data Frames

FUNCTIONS USED IN DATAFRAME

Creating dataframe for our project:

import pandas as pd

import numpy as np

data={“Name”:[“Sumit”,”Aditya”,”Pravesh”,”Sandeep”,”Kuldeep”,”Vivek”,”Palak”,”Neha”,”Shivam”,”Anjali”,”Ritu”],

“Sports”:[“Football”,”Circket”,”Circket”,”Athletics”,”Chess”,”Football”,”Batminton”,”Batminton”,”Cirket”,”Kho-Kho”,”Athletics”],

“Subject”:[“IP”,”IP”,”IP”,”HINDI”,”IP”,”MATHS”,”IP”,”IP”,”HINDI”,”HINDI”,”HINDI”],

“class 10 result(%)”:[70,65,72,71,64,68,91,73,65,66,65]

}

Dataframe1=pd.DataFrame(data=data,index=range(1,12))

print(dataf

DFO = DataFrame Object

S.no	FUNCTION	USE	EXAMPLE
1.	<DFO >.loc[<start row>:<end row>, <start column>: <end column>]]	To access a subset from a DataFrame using Row/Colum names.	dataframe.loc[1:5,”Name”:”Sports”]
2.	<DFO>.iloc[<start row index>:<end row index>, <start col index>:<end col index>]	To obtain a slice from a DataFrame using row/column numeric index/position.	dataframe.iloc[0:6,0:2]
3.	<DFO>.at[<row name>,<column name>]	To access a single value for a row/column name pair.	dataframe.at[5,”class 10 result(%)”]
4.	<DFO>.iat[<row index no>,<col index number>]	To access single value for a row/column pair by integer position.	dataframe.iat[5,3]
5.	<DFO>.drop(index or sequence of index)	To delete row from a dataframe.	dataframe.drop(5)
6.	<DFO>.iterrows()	TO process all the data values of a dataframe. It use row .	for (row,rowseries) in dataframe.iterrows():                 print(“row index:\t\n”,row)                 print(“contaning\n\n”,rowseries)
7.	<DFO>.iteritems()	To process all the data values of a dataframe using colums in once.	for (itemno,itemdata) in dataframe.iteritems():                 print(“column index:\t\n”,itemno)                 print(“contaning\n\n”,itemdata)
8.	<DFO>.add(<DFO1>)	To add data of two dataframes. radd() for adding revese. You can use + also	dataframe.add(dtf1)
9.	<DFO>.sub(<DFO1>)	To subtract data of two dataframes. rsub() for subtracting reverse. You can also use -.	dataframe.sub(dtf1)
10.	<DFO>.mul(<DFO1>)	To multiply the data of two dataframes. You can also use *.	dataframe.mul(dtf1)
11.	<DFO>.div(<DFO1>)	To divide data of two dataframes. You can also use /.	dataframe.div(dtf1)
12.	<DFO>.info()	It give you basic information about your datafarme object. It give you detail about : 1.type. 2.index values. 3.number of rows. 4.data columns and values in them. 5. Datatype of each colums. 6.memory usage.	dataframe.info()
13.	<DFO>.describe()	It also give detail of data but about: 1.counting of NaN values in a column 2.Mean 3.standard deviation 4.percentile 5.minmum value 6. maximum value All detail about column.	dataframe.describe()
14.	<DFO>.cumsum([axis=noun])	To cumulative sum of row or colums .	For rows: dataframe.cumsum(axis=”rows”) For columns: dataframe.cumsum(axis=”columns”)
15.	<DFO>.dropna()	To remove missing values from the data.	dataframe.dropna()
16.	<DFO>.fillna()	To fill the missing values.	dataframe.fillna()
17.	<DFO>.empty	It return the Boolean value if dataframe is empty than true otherwise false	dataframe.empyt
18.	<DFO>.any()	This function returns true if any element is true.	dataframe.any()
19.	<DFO>.all()	This function returns true If all values are true.	dataframe.all()
20.	<DFO>.combine_first(<DFO1>)	This function combines the two dataframe.	dataframe.combine_first(dtf1)
21.	pandas.concat([<DF1>,<DF2>])	This function also combine the two dataframes.	Newdf=pd.concat([dataframe,df1])