How to Create A Calculated Column In Pandas?

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

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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.

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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:

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:

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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.