WebMar 6, 2024 · How to Filter Pandas DataFrame by multiple conditions? By using df[], loc[], query(), eval() and numpy.where() we can filter Pandas DataFrame by multiple conditions. The process of applying multiple filter conditions in Pandas DataFrame is one of the most frequently performed tasks while manipulating data. WebMar 16, 2024 · set value of column dataframe based on two other columns pandas add column based on condition of other columns add two column conditions pandas pandas assign value to multiple column based on condition pandas apply condition of two columns. and two columns pandas create dataframe with 2 columns create new column …
Filter Pandas Dataframe with multiple conditions
WebAug 9, 2024 · I am trying to generate a new column on my existing dataframe that is built off conditional statements with the input being data from multiple columns in the dataframe. I'm using the np.select() method as I read this is the best way to use multiple columns as inputs to levels of conditions. WebMar 31, 2024 · Judging by the image of your data is rather unclear what you mean by a discount 20%.. However, you can likely do something like this. df['class'] = 0 # add a class column with 0 as default value # find all rows that fulfills your conditions and set class to 1 df.loc[(df['discount'] / df['total'] > .2) & # if discount is more than .2 of total (df['tax'] == 0) & … chin woo
numpy where with multiple conditions linked to dataframe
WebJul 16, 2024 · doesn’t allow nested conditions; 6. Nested np.where() — fast and furious. np.where() is a useful function designed for binary choices. You can nest multiple np.where() to build more complex ... Web2 days ago · def slice_with_cond(df: pd.DataFrame, conditions: List[pd.Series]=None) -> pd.DataFrame: if not conditions: return df # or use `np.logical_or.reduce` as in cs95's answer agg_conditions = False for cond in conditions: agg_conditions = agg_conditions cond return df[agg_conditions] Then you can slice: Webnumpy.select. This is a perfect case for np.select where we can create a column based on multiple conditions and it's a readable method when there are more conditions:. conditions = [ df['gender'].eq('male') & df['pet1'].eq(df['pet2']), df['gender'].eq('female') & df['pet1'].isin(['cat', 'dog']) ] choices = [5,5] df['points'] = np.select(conditions, choices, … chinwo mercy chinedum