Course Content
Pandas First Steps
Pandas First Steps
sum() and count()
Pandas offers the count()
method, which counts all non-null cells (neither None nor np.NaN) for each column. Consider the following example:
To find the count of non-null values in a specific column, use:
Pandas also provides the sum()
method. This function calculates the sum of values for each column, but it only works with numeric columns. Check out the following example:
To find the sum of values in a particular column:
Task
Given the dataframe audi_cars
:
- Determine the count of non-null cells in each column.
- Compute the total price (using the
price
column) for all cars in the DataFrame.
Task
Given the dataframe audi_cars
:
- Determine the count of non-null cells in each column.
- Compute the total price (using the
price
column) for all cars in the DataFrame.
Everything was clear?
sum() and count()
Pandas offers the count()
method, which counts all non-null cells (neither None nor np.NaN) for each column. Consider the following example:
To find the count of non-null values in a specific column, use:
Pandas also provides the sum()
method. This function calculates the sum of values for each column, but it only works with numeric columns. Check out the following example:
To find the sum of values in a particular column:
Task
Given the dataframe audi_cars
:
- Determine the count of non-null cells in each column.
- Compute the total price (using the
price
column) for all cars in the DataFrame.
Task
Given the dataframe audi_cars
:
- Determine the count of non-null cells in each column.
- Compute the total price (using the
price
column) for all cars in the DataFrame.
Everything was clear?
sum() and count()
Pandas offers the count()
method, which counts all non-null cells (neither None nor np.NaN) for each column. Consider the following example:
To find the count of non-null values in a specific column, use:
Pandas also provides the sum()
method. This function calculates the sum of values for each column, but it only works with numeric columns. Check out the following example:
To find the sum of values in a particular column:
Task
Given the dataframe audi_cars
:
- Determine the count of non-null cells in each column.
- Compute the total price (using the
price
column) for all cars in the DataFrame.
Task
Given the dataframe audi_cars
:
- Determine the count of non-null cells in each column.
- Compute the total price (using the
price
column) for all cars in the DataFrame.
Everything was clear?
Pandas offers the count()
method, which counts all non-null cells (neither None nor np.NaN) for each column. Consider the following example:
To find the count of non-null values in a specific column, use:
Pandas also provides the sum()
method. This function calculates the sum of values for each column, but it only works with numeric columns. Check out the following example:
To find the sum of values in a particular column:
Task
Given the dataframe audi_cars
:
- Determine the count of non-null cells in each column.
- Compute the total price (using the
price
column) for all cars in the DataFrame.