Introduction to Data Science with Python/

...

/

Solution 2: Pandas Essentials

Solution 2: Pandas Essentials

Let’s look at the solution related to NumPy essentials.

We'll cover the following...

Task 1: Count of NaN in the DataFrame

Calculate how many NaN we have in our dataset.

Hint: use isnull().

Solution

Press + to interact
def number_of_nulls_in_each_column():
  # Your Code Here
  list =  pay.isnull().sum()
  print("Nans in each column")
  print(list)
  nans = []
  for nan in list:
    nans.append(nan)
  return sum(nans)
print("Total Number of Nans in our DataFrame : ", number_of_nulls_in_each_column())

Explanation

We use the ...