sum() and count()
pandas
offers the count()
method, which counts all non-null cells (neither None
nor NaN
) for each column.
df = pd.read_csv(file.csv)
number_of_cells = df.count()
To find the count of non-null values in a specific column, use the following syntax:
df = pd.read_csv(file.csv)
number_of_cells = df['name of the column'].count()
pandas
also provides the sum()
method. This method calculates the sum of values for each column, but it only works with numeric or boolean columns.
df = pd.read_csv(file.csv)
total = df.sum()
Since the isna()
method returns a boolean DataFrame, you can use the following syntax to calculate the number of missing values for each of the columns:
missing_values_count = df.isna().sum()
To find the sum of values in a particular column, use the following syntax:
df = pd.read_csv(file.csv)
total = df['name of the column'].sum()
Task
Swipe to start coding
You are given a DataFrame
named audi_cars
.
- Get the count of non-null cells in each column and store the result in the
number_of_cells
variable. - Compute the total price (using the
'price'
column) for all cars in theDataFrame
and store the result in thetotal_price
variable. - Identify the number of missing values in each column and store the result in the
null_count
variable.
Solution
Everything was clear?
Thanks for your feedback!