[DATAFRAME ] Class XII access rows and columns using different methods

To work with columns and rows in a DataFrame using Python, you'll typically use the pandas library, which provides powerful data manipulation tools. Below is a basic example to demonstrate how to create a DataFrame, access its columns and rows, and perform some common operations. How to access columns and rows in DataFrame.

Key Points:

1. Accessing Columns:

   • You can access a single column using df['ColumnName'].
   • Multiple columns can be accessed using df[['Column1', 'Column2']].

2. Accessing Rows:

   • Use df.iloc[index] for accessing rows by their integer location.
   • Use df.loc[index] for accessing rows by their label/index.

3. Accessing Specific Elements:

   • df.at[row_index, 'ColumnName'] accesses a specific element based on row and column labels.

4. Filtering Rows:

• You can filter rows based on a condition, such as df[df['Age'] > 25] to get all rows where the 'Age' column has values greater than 25.

# Accessing column Single Rows and multiple rows

import pandas as pd

# Sample data
data = {
    'Name': ['Alice', 'Bob', 'Charlie', 'David'],
    'Age': [24, 27, 22, 32],
    'City': ['New York', 'Los Angeles', 'Chicago', 'Houston']
}

# Create DataFrame
df = pd.DataFrame(data)

# Display the DataFrame
print("DataFrame:")
print(df)

# Accessing columns
print("\nAccessing the 'Name' column:")
print(df['Name'])

# Accessing multiple columns
print("\nAccessing the 'Name' and 'City' columns:")
print(df[['Name', 'City']])




# Accessing column Single  and multiple columns

import pandas as pd

# Sample data
data = {
    'Name': ['Alice', 'Bob', 'Charlie', 'David'],
    'Age': [24, 27, 22, 32],
    'City': ['New York', 'Los Angeles', 'Chicago', 'Houston']
}
# Create DataFrame
df = pd.DataFrame(data)
# Display the DataFrame
print("DataFrame:")
print(df)
print(" Accessing columns ")
print(df.Name)











# Accessing row using Default index Single Rows and 
multiple rows
import pandas as pd

# Sample data
data = {
    'Name': ['Alice', 'Bob', 'Charlie', 'David'],
    'Age': [24, 27, 22, 32],
    'City': ['New York', 'Los Angeles', 'Chicago', 'Houston']
}

# Create DataFrame
df = pd.DataFrame(data)

# Accessing rows using iloc (integer-location based indexing)
print("\nAccessing the first row:")
print(df.iloc[0])

print("\nAccessing the Multiple rows:")
print(df.iloc[0:3])

# Accessing row using given index Single Rows and

multiple rows

import pandas as pd

# Sample data

data = {

    'Name': ['Alice', 'Bob', 'Charlie', 'David'],

    'Age': [24, 27, 22, 32],

    'City': ['New York', 'Los Angeles', 'Chicago', 'Houston']

}

# Create DataFrame

df = pd.DataFrame(data)

print(df)

# Accessing rows using loc (label based indexing)

print("\nAccessing the row where index is 1:")

print(df.loc[1])

print("\nAccessing the rows where index is 1:3")

print(df.loc[1:3])

# Accessing row and column or specific values

import pandas as pd

# Sample data

data = {

    'Name': ['Alice', 'Bob', 'Charlie', 'David'],

    'Age': [24, 27, 22, 32],

    'City': ['New York', 'Los Angeles', 'Chicago', 'Houston']

}

# Create DataFrame

df = pd.DataFrame(data)

print(df)

# Accessing a specific element (row and column)

print("\nAccessing the element in the second row and 'City' column:")

print(df.at[1, 'City'])

# Accessing row and column or specific values

import pandas as pd

# Sample data

data = {

    'Name': ['Alice', 'Bob', 'Charlie', 'David'],

    'Age': [24, 27, 22, 32],

    'City': ['New York', 'Los Angeles', 'Chicago', 'Houston']

}

# Create DataFrame

df = pd.DataFrame(data)

print(df)

# Accessing a specific element (row and column)

print("\nAccessing the element in the second row and 0 column:")

print(df.iat[1, 0])

print("\nAccessing the element in the 3 row and 2 column:")

print(df.iat[2, 2])