CLASS XII DataFrame Count and Size

 

Certainly! In Python, you can use the pandas library to handle data in DataFrames. Below are examples of how you can get the size and count of elements in a DataFrame using pandas.

1. Getting the Size of a DataFrame (include NaN and None Values)

To get the size (i.e., the total number of elements) in a DataFrame, you can use the size attribute.

python
import pandas as pd

# Sample DataFrame
data = {
    'A': [1, 2, 3],
    'B': [4, 5, 6],
    'C': [7, 8, 9]
}
df = pd.DataFrame(data)

# Get the size of the DataFrame
size = df.size
print("Size of the DataFrame:", size)

2. Getting the Count of Non-NA/null Entries  (Exclude NaN and None Values)

To get the count of non-NA/null entries for each column, you can use the count() method.

import pandas as pd

# Sample DataFrame
data = {
    'A': [1, 2, None],
    'B': [4, None, 6],
    'C': [None, 8, 9]
}
df = pd.DataFrame(data)

# Get the count of non-NA/null entries for each column
count_non_na = df.count()
print("Count of non-NA/null entries for each column:")
print(count_non_na)

print("df.count(0)\n",df.count(0))
print("df.count(1)\n",df.count(1))




In pandas, the count() method is used to count the number of 
non-NA/null entries in each column or row of a DataFrame. 
This method is very useful for understanding how much data you 
have in each part of your DataFrame, especially when dealing with 
missing values.
df.count(): Counts non-NA/null entries in each column.
df.count(axis=1): Counts non-NA/null entries in each row.
df.count(axis=0): Counts non-NA/null entries along columns (default).
Usage of the count() Function
Here's how you can use the count() method with a DataFrame:
1. Count Non-NA/null Entries in Each Column
By default, df.count() counts the non-NA/null entries for each column.
import pandas as pd

# Sample DataFrame
data = {
    'A': [1, 2, None],
    'B': [4, None, 6],
    'C': [None, 8, 9]
}
df = pd.DataFrame(data)

# Count of non-NA/null entries in each column
count_per_column = df.count()
print("Count of non-NA/null entries for each column:")
print(count_per_column)
Output:
Count of non-NA/null entries for each column:
A    2
B    2
C    2
dtype: int64
2. Count Non-NA/null Entries in Each Row
You can use the axis=1 parameter to count the non-NA/null 
entries for each row.
python
import pandas as pd

# Sample DataFrame
data = {
    'A': [1, 2, None],
    'B': [4, None, 6],
    'C': [None, 8, 9]
}
df = pd.DataFrame(data)

# Count of non-NA/null entries in each row
count_per_row = df.count(axis=1)
print("Count of non-NA/null entries for each row:")
print(count_per_row)
Output:
Count of non-NA/null entries for each row:
0    2
1    2
2    2
dtype: int64
3. Count Non-NA/null Entries for Specific Axis
You can use the axis parameter to specify whether you want to count 
entries along the rows or columns:
axis=0 (or omit axis parameter): Counts along columns (default).
axis=1: Counts along rows.
Example of counting along columns (default behavior):
python
import pandas as pd

# Sample DataFrame
data = {
    'A': [1, 2, None],
    'B': [4, None, 6],
    'C': [None, 8, 9]
}
df = pd.DataFrame(data)

# Count non-NA/null entries along columns
count_columns = df.count(axis=0)
print("Count of non-NA/null entries along columns:")
print(count_columns)
Example of counting along rows:
python

import pandas as pd

# Sample DataFrame
data = {
    'A': [1, 2, None],
    'B': [4, None, 6],
    'C': [None, 8, 9]
}
df = pd.DataFrame(data)

# Count non-NA/null entries along rows
count_rows = df.count(axis=1)
print("Count of non-NA/null entries along rows:")
print(count_rows)