To add a new row in a DataFrame for class 12, you can use the loc
method if you know the index, or the append
method if you want to add it without specifying an index. Here is an example using both methods:
Using loc
:
import pandas as pd
# Existing DataFrame
data = {
'Name': ['Alice', 'Bob'],
'Class': [10, 11]
}
df = pd.DataFrame(data)
# Add a new row for Class 12 using loc
df.loc[len(df)] = ['Charlie', 12]
print(df)
This methods will give you the following DataFrame:
Name Class
0 Alice 10
1 Bob 11
2 Charlie 12
import pandas as pd
# Existing DataFrame
data = {
'Name': ['Alice', 'Bob'],
'Class': [10, 11]
}
df = pd.DataFrame(data)
print(df,"\n \n")
# Add a new row for Class 12 using loc
df.loc[len(df)] = ['Charlie', 12]
df.loc[3] = ['Charlie', 12]
print(df)
Output
Explanation:
- Using
loc
:df.loc[len(df)] = ['Charlie', 12]
adds a new row at the end of the DataFrame.
Add new Column in Existing DataFrame
To add a new column to a DataFrame, you can simply assign a new list or a single value to the DataFrame with the new column name. Here’s how you can do it:
Example
import pandas as pd
# Existing DataFrame
data = {
'Name': ['Alice', 'Bob'],
'Class': [10, 11]
}
df = pd.DataFrame(data)
# Add a new column for Class 12
df['Class 12'] = [None, None] # Initialize with None or some default values
print(df)
For the first example:
Name Class Class 12
0 Alice 10 None
1 Bob 11 None
If you have specific values for each row in the new column, you can assign those values directly:
import pandas as pd
# Existing DataFrame
data = {
'Name': ['Alice', 'Bob'],
'Class': [10, 11]
}
df = pd.DataFrame(data)
# Add a new column for Class 12 with specific values
df['Class 12'] = ['A+', 'B']
print(df)
Result
For the second example:
Name Class Class 12
0 Alice 10 A+
1 Bob 11 B
Explanation:
- Adding a new column with default values:
df['Class 12'] = [None, None]
initializes the new column withNone
for each row. - Adding a new column with specific values:
df['Class 12'] = ['A+', 'B']
initializes the new column with specific values for each row.
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