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How to Check Column Nulls and Replace: Pandas

Here is a post that shows how to count Nulls and replace them with the value you want in the Pandas Dataframe. We have explained the process in two steps - Counting and Replacing the Null values. Count null values (column-wise) in Pandas ## count null values column-wise null_counts = df.isnull(). sum() print(null_counts) ``` Output: ``` Column1    1 Column2    1 Column3    5 dtype: int64 ``` In the above code, we first create a sample Pandas DataFrame `df` with some null values. Then, we use the `isnull()` function to create a DataFrame of the same shape as `df`, where each element is a boolean value indicating whether that element is null or not. Finally, we use the `sum()` function to count the number of null values in each column of the resulting DataFrame. The output shows the count of null values column-wise. to count null values column-wise: ``` df.isnull().sum() ``` ##Code snippet to count null values row-wise: ``` df.isnull().sum(axis=1) ``` In the above code, `df` is the Panda

6 Exclusive List and Tuple Differences in Python

Here're quick differences between List and Tuple

Here're the quick differences between Tuple and List in Python. These are helpful for interviews and your project.

Tuple and List differences


  • Comma-separated elements inside a square bracket [] make a list.
  • The elements are indexed, which starts from '0'
  • These you need to enclose in a single quote and separate by a comma.
  • It can contain another list, which is called a NESTED list.
  • Use type() function to get the type of data it is.
  • The list is mutable (you can change the data). The objects (elements) can be of different data types. Here're examples on the List.


  • The elements comma-separated and enclosed in parenthesis () 
  • The elements are indexed, which starts from '0'
  • It can have heterogeneous data (integer, float, string, list, etc.)
  • It is immutable. So you can't change the elements.
  • Use the type() function to get the type of data it is. 
  • Here're examples of Tuple.

List Example

#Illustration of creating a list 
new_list=[1, 2, 3, 4] 

# Homogeneous data elements 
new_list1=[1, "John", 55.5] 

# Heterogeneous data elements 
new_list2=[111, [1, "Clara", 75.5]] 
# Nested list 


[1, 2, 3, 4]
[1, ‘John’, 55.5]
[111, [1, ‘Clara’, 75.5]]

Tuple Example

#Illustration of unpacking a tuple 
 new_tuple2=(111, [1, "Clara", 75.5], (2, "Simon", 80.5)) 

# Nested tuple 
print(new_tuple2) x, y, z=new_tuple2 


[1, ‘Clara’, 75.5]
(2, ‘Simon’, 80.5)


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