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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()
8 lip 2021 · Here is a solution that can be extended easily to several columns. use filter with regex='Found\d' to select all the columns Found in your dataframe. check not equal (ne) to the empty string and sum along the columns to get the Want_Sum column.
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.
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)
=IF( COUNTIF(BM2:BQ2,"")=5, "", "Major Milestone Due") where 5 is the size of your range. This method does not work as well with dynamic ranges. A slightly more complex formula (at least, more complex to explain!) uses SUMPRODUCT: =IF( SUMPRODUCT(--(BM2:BQ2<>""))=0, "", "Major Milestone Due") (Note that COUNTIF(BM2:BQ2,"<>") has the same issue ...
2 lut 2023 · One caveat with ISBLANK() if there are formulas in the cells that return an empty string ("") then ISBLANK will not work here as it will return FALSE. For those instances (and this one too): =SUMPRODUCT((C2:C12="")*(B2:B12<>""))
11 lip 2024 · To check if specific cells in a DataFrame are empty (or null), you can use the isnull() method, which returns a DataFrame of the same size containing True for empty or NaN entries and False otherwise:
