Working with Data Frame in Pandas

Working with data frame in pandas python involve manipulation of rows and columns of data frame by different methods which are described  below

Working with Data Frame in Pandas Python:

First we create a dataframe as shown below which has 3 columns and 3 rows.For details what is data frame you can click on series and dataframe in python

1.How to create Data Frame:

df = pd.DataFrame(
{
"Name": [
"Harris",
" William",
"Bonnell"]
,
"Age": [18, 20, 40],
"Sex": ["male", "female", "female"],
}
)
Output:
Name	Age	Sex
0	Harris	18	male
1	William	20	female
2	Bonnell	40	female

2.Filtering & Selecting in Data Frame:

Shape of data frame can be checked by typing df.shape as shown below

df.shape
Output:
(3,3)
#3 rows and 3 columns

3.For selecting name and age column only:

df[['Name','Age']]
Output:
Name	Age
0	Harris	18
1	William	20
2	Bonnell	40

4. For selecting rows only by name and age:

df[(df['Name']==' William')]
Output=  Name	Age	Sex
1	William	20	female
# age is greater than 30
df[df['Age']>30]
Output=  Name	Age	Sex
2	Bonnell	40	female

5.Select rows and columns by index using iloc:

df.iloc(rowindex,colindex)
df.iloc[1:3,0:2]
#it will output row 1:3(2nd and 3rd index),Columns(1st and 2nd index)
Output:
Name	Age
1	William	20
2	Bonnell	40

6. Select rows and columns with any criteria using loc:

# if you want to  find names of the person who has age greater than 30
df.loc[df['Age']>30,'Name']
Output:
2    Bonnell

Above  methods are used for working with data frame in pandas python.