Q.What do you understand by the size of
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Start your 14-day free trial to unlock the full solution →The size attribute returns the total number of elements in a pandas object — for a Series it's the number of rows, and for a DataFrame it's rows × columns.
In pandas, the size attribute is a quick way to get the total count of elements in a data structure. It's not the same as shape (which gives dimensions) or len() (which gives only the row count). The key idea is that size flattens the structure into a single number: every cell counts.
(i) Size of a Series
A Series is a one-dimensional labelled array. Its size is simply the number of elements (rows) it contains. For example:
import pandas as pd
s = pd.Series([10, 20, 30, 40])
print(s.size) # Output: 4
If the Series has missing values (NaN), those still count as elements — size counts the total number of positions, not just non-null values. Use count() if you want only non-null entries.
For a Series, size is equivalent to len(s) and to s.shape[0]. All three give the same result because a Series has only one dimension.
(ii) Size of a DataFrame
A DataFrame is a two-dimensional labelled data structure (rows and columns). Its size is the total number of cells: number of rows × number of columns. For instance:
import pandas as pd
df = pd.DataFrame({'A': [1, 2], 'B': [3, 4], 'C': [5, 6]})
print(df.size) # Output: 6 (2 rows × 3 columns) …
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