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

```
1
``` |
```
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:

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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:

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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:

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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:

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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:

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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:

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

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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.