Search results
Code below uses regex to replace blanks with NaN. And pandas count for non-NA cells. # Import library import pandas as pd # Create DataFrame newDF = pd.DataFrame({ 'Paid_Off_In_Days':[1, np.nan, 15, ' ', 18, 29] }) # Regex to replace blanks with NaN newDF = newDF.replace(r'^\s*$', np.nan, regex=True) # Get counts counts = newDF.count()
I'd do this with just count: train_df[sites].count(axis=1) count specifically counts non-null values. The issue with your current implementation is that notnull yields boolean values, and bools are certainly not-null, meaning they are always counted.
1 lut 2024 · To count values that meet a condition in any row or column of a DataFrame, specify the row or column using [], loc[], iloc[], and perform the same process. pandas: Select rows/columns by index (numbers and names) pandas: Get/Set values with loc, iloc, at, iat; Multiple conditions (AND, OR, NOT)
20 lut 2024 · The simplest way to count non-NA/null values across each column is to use the count() method: # Counting non-null values in each column. df.count() This method directly provides the count of non-null values per column. However, it does not include row-wise counts or differentiate between data types.
In this tutorial, we will learn how to write an Excel formula in Python that counts the number of non-empty cells in a column. We will use the COUNTA function, which is a built-in function in Excel. The COUNTA function counts the number of cells in a range that are not empty.
Learn how to write an Excel formula in Python to count the number of non-empty cells based on specific criteria.
Learn how to write an Excel formula in Python to count non-empty cells in a specific column based on a value in another cell.
