I want to do the following: index A1 0 1 1 2 -8 3 Hello 4 to: index A1 1 4 Is there like a reverse .dropna function? >Solution : Assuming real NaNs: out = df[df[‘A1’].isna()] If empty strings: out = df[df[‘A1’].eq(”)] For both: out = df[df[‘A1’].fillna(”).eq(”)]

Is there direct possibility to drop all columns before a matching string in a pandas Dataframe. For eg. if my column 8 contains a string ‘Matched’ I want to drop columns 0 to 7 ? >Solution : Well, you did not give any information where and how to look for ‘Matched’, but let’s say that… Read More Dropping columns before a matching string in the following column

I have a data table with containing tuples of words. I want to drop the rows (pf tuple) that contains no words ("[]"). Here’s what my data looks like, in which I expect 3rd row to be removed in the new dataset. stemming 0 [go, experience] 1 [real] 2 [] 3 [love, colour, tabs] Here’s… Read More Drop rows of tuples containing null value

I can’t understand why i get the error "IndexError: index 159 is out of bounds for axis 0 with size 159" while dropping a list of rows from a dataframe. #Import file Excel xls = pd.ExcelFile(file_path) #Parse away the first 5 rows df = xls.parse(‘Daten’, skiprows=5, index_col=None, na_values=[‘NA’]) # Select row where value in column… Read More Index out of bound when dropping rows in a dataframe

I have a DataFrame that has a Collumn (AE) that o could contains: nothing (""), "X", "A" or "E". I want to drop all the rows that HAS and "X" on it. I searching here on StackOverflow I have found 2 ways of doing it: df= df.drop(df[df.AE == "X"].index) or df=df[df["AE"] != "X"] But for… Read More Different ways to conditional Drop Row in Pandas