To create a calculated column in pandas, you can use the following syntax:

df['new_column'] = df['existing_column1'] * df['existing_column2']

In this example, we are creating a new column called 'new_column', which is the result of multiplying two existing columns 'existing_column1' and 'existing_column2'. You can perform any mathematical operation or apply a function to create a new column based on existing columns in the DataFrame.

How to create a column that aggregates data from other columns in pandas?

To create a new column in a pandas data frame that aggregates data from other columns, you can use the .apply() function along with a custom function. Here's an example of how to create a new column that sums the values from two existing columns:

import pandas as pd

Create a sample data frame

data = {'A': [1, 2, 3], 'B': [4, 5, 6]} df = pd.DataFrame(data)

Create a custom function to calculate the sum of two columns

def sum_columns(row): return row['A'] + row['B']

Use the .apply() function to apply the custom function to each row

df['C'] = df.apply(sum_columns, axis=1)

print(df)

In this example, we define a custom function sum_columns that takes a row as input and returns the sum of the 'A' and 'B' columns. We then use the .apply() function along with axis=1 to apply the sum_columns function to each row in the data frame and create a new column 'C' that contains the aggregated data.

You can modify the custom function to aggregate data in different ways depending on your requirements.

How to add a new column to a pandas dataframe?

To add a new column to a pandas dataframe, you can simply assign values to a new column label. Here's an example:

import pandas as pd

Create a dataframe

data = {'A': [1, 2, 3, 4, 5], 'B': [10, 20, 30, 40, 50]} df = pd.DataFrame(data)

Add a new column 'C' with values [100, 200, 300, 400, 500]

df['C'] = [100, 200, 300, 400, 500]

print(df)

This will output:

A B C 0 1 10 100 1 2 20 200 2 3 30 300 3 4 40 400 4 5 50 500

You can also use various methods to add a new column based on existing columns in the dataframe using arithmetic operations or functions.

How to perform arithmetic operations in a pandas dataframe?

You can perform arithmetic operations on a pandas dataframe using the basic arithmetic operators like + (addition), - (subtraction), * (multiplication), and / (division).

Here is an example of how to perform arithmetic operations on a pandas dataframe:

import pandas as pd

Create a sample dataframe

data = {'A': [1, 2, 3, 4], 'B': [5, 6, 7, 8]}

df = pd.DataFrame(data)

Add a constant value to each element in column 'A'

df['A'] = df['A'] + 10

Subtract a constant value from each element in column 'B'

df['B'] = df['B'] - 3

Multiply each element in column 'A' by 2

df['A'] = df['A'] * 2

Divide each element in column 'B' by 2

df['B'] = df['B'] / 2

print(df)

This will output:

A    B

0 22 1.0 1 24 1.5 2 26 2.0 3 28 2.5

What are some common functions used in creating calculated columns in pandas?

Some common functions used in creating calculated columns in pandas include:

1. Arithmetic operations: Addition (+), subtraction (-), multiplication (*), division (/), and modulus (%).
2. Comparison operators: Greater than (>), less than (<), equal to (==), not equal to (!=), greater than or equal to (>=) and less than or equal to (<=).
3. Logical operators: AND (&), OR (|), NOT (~).
4. Mathematical functions: abs(), round(), ceil(), floor(), log(), exp(), sin(), cos(), tan(), sqrt().
5. Text functions: str.lower(), str.upper(), str.startswith(), str.endswith(), str.contains().
6. Date functions: pd.to_datetime(), pd.date_range(), pd.to_timedelta().
7. Combining columns: Concatenation with + or pd.concat(), merging with pd.merge(), joining with pd.join().
8. Conditional statements: np.where(), pd.apply(), pd.eval().
9. Grouping and aggregating: groupby(), sum(), count(), mean(), max(), min(), std(), var().
10. Reshaping data: pivot_table(), melt(), stack(), unstack().

How to create a column with string manipulation in pandas?

To create a new column with string manipulation in pandas, you can use the str accessor on a pandas Series object. Here is an example of how to create a new column by concatenating two columns:

import pandas as pd

Sample DataFrame

data = {'Name': ['John Doe', 'Jane Smith', 'Tom Brown'], 'Age': [30, 25, 35]} df = pd.DataFrame(data)

Create a new column by concatenating 'Name' and 'Age' columns

df['Full Name'] = df['Name'] + ' - ' + df['Age'].astype(str)

print(df)

In this example, we are using the + operator to concatenate the 'Name' and 'Age' columns together and create a new column called 'Full Name'. You can also perform various other string manipulations using the str accessor, such as extracting substrings, replacing values, converting case, etc.