Python
import pandas as pd
data = {'Name': ['John', 'Jane', 'Sam'],
        'Age': [25, 30, 35],
        'City': ['New York', 'London', 'Paris']}
df = pd.DataFrame(data)
print(df)
Name  Age      City
0  John   25  New York
1  Jane   30    London
2   Sam   35     Paris
Python
import pandas as pd
data = [10,20,30,40,50]
s = pd.Series(data)
print(s[2])
30
Python
import pandas as pd
data= [10,20,30,40,50]
s = pd.Series(data,index = ['A','B','C','D','E' ])
print(s['C'])
30
Python
import pandas as pd
data= [10,20,30,40,50]
s = pd.Series(data)
print(s[1:4])
1    20
2    30
3    40
dtype: int64
Python
import pandas as pd
data= [10,20,30,40,50]
s = pd.Series(data)
print(s[s > 30])
3    40
4    50
dtype: int64
Python
import pandas as pd
data= [10,20,30,40,50]
index_labels =["A","B","C","D","E"]
s = pd.Series(data,index=index_labels,dtype=int,name="myseries")
print(s)
A    10
B    20
C    30
D    40
E    50
Name: myseries, dtype: int32
Python
import pandas as pd
data= [10,20,30,40,50]
s=pd.Series(data)
s[1]=100
print(s)
0     10
1    100
2     30
3     40
4     50
dtype: int64
Python
import pandas as pd
data = {
        'Name': ['John', 'Jane', 'Sam'],
        'Age' : [25, 30, 35],
        1     : ['New York', 'London', 'Paris']
}
df = pd.DataFrame(data)
print(df)
Name  Age         1
0  John   25  New York
1  Jane   30    London
2   Sam   35     Paris
Python
import pandas as pd
data = {
        'Name': ['John', 'Jane', 'Sam'],
        'Age' : [25, 30, 35],
        'City'    : ['New York', 'London', 'Paris']
}
df = pd.DataFrame(data, columns=['Name','Age','city'])
print(df)
Name  Age city
0  John   25  NaN
1  Jane   30  NaN
2   Sam   35  NaN
Python
import pandas as pd 
data = {
        'Name': ['John', 'Jane', 'Sam'],
        'Age' : [25, 30, 35],
        'City'    : ['New York', 'London', 'Paris']
}
df = pd.DataFrame(data)
print(df['Name'])
0    John
1    Jane
2     Sam
Name: Name, dtype: object
Python
# ("Indexing Rows by Label:")
import pandas as pd 
data = {
        'Name': ['John', 'Jane', 'Sam'],
        'Age' : [25, 30, 35],
        'City'    : ['New York', 'London', 'Paris']
}
df = pd.DataFrame(data)
print(df.loc[1])
Name      Jane
Age         30
City    London
Name: 1, dtype: object
Python
# Indexing rows by integer index.
import pandas as pd 
data = {
        'Name': ['John', 'Jane', 'Sam'],
        'Age' : [25, 30, 35],
        'City'    : ['New York', 'London', 'Paris']
}
df = pd.DataFrame(data)
print(df.iloc[2])
Name      Sam
Age        35
City    Paris
Name: 2, dtype: object
Python
# Indexing with Conditions

import pandas as pd 
data = {
        'Name': ['John', 'Jane', 'Sam'],
        'Age' : [25, 30, 35],
        'City'    : ['New York', 'London', 'Paris']
}
df = pd.DataFrame(data)
rows =df[df['Age'] > 30]
print(rows)
Name  Age   City
2  Sam   35  Paris
Python
# Slicing Rows
import pandas as pd 
data = {
        'Name': ['John', 'Jane', 'Sam'],
        'Age' : [25, 30, 35],
        'City'    : ['New York', 'London', 'Paris']
}
df = pd.DataFrame(data)
rows = df[1:3]
print(rows)
Name  Age    City
1  Jane   30  London
2   Sam   35   Paris
Python
import pandas as pd 
data = {
        'Name': ['John', 'Jane', 'Sam'],
        'Age' : [25, 30, 35],
        'City'    : ['New York', 'London', 'Paris']
}
df = pd.DataFrame(data)
rows = df[ : , 1: ]
print(rows)
line 8, in <module>
    rows = df[ : , 1: ]
           ~~^^^^^^^^^^
  File "/lib/python3.12/site-packages/pandas/core/frame.py", line 4090, in __getitem__
    indexer = self.columns.get_loc(key)
              ^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/lib/python3.12/site-packages/pandas/core/indexes/base.py", line 3808, in get_loc
    raise InvalidIndexError(key)
pandas.errors.InvalidIndexError: (slice(None, None, None), slice(1, None, None))