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])