Working with Data Frame in Pandas

To work with dataframe rows and columns different methods can be used as described below

1.First we create a dataframe as shown below which has 3 columns and 3 rows.

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

Filtering & Selecting Data Frame

2.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

6.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

7. 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