I would like to apply a function to each element/row of a pandas Series/DataFrame and concatenate/stack the results into a single DataFrame. E.g., I may start with a Series s = pd.Series(["a", "b,c", "d,e,f"]), and I would like to obtain as a final result res = pd.Series(["a", "b", "c", "d", "e", "f"]) A slow way… Read More Concatenate results of `apply` in pandas
I have a column of basketball players height: 0 6-10 1 6-9 2 7-2 3 6-1 4 6-6 … 4545 6-11 4546 7-1 4547 6-1 4548 7-1 4549 6-3 I want to convert the values from feet to cm. I made a split: player_data[‘height’].str.split(‘-‘), and received a Series of arrays with separate feet and inches:… Read More converting feet-inch string DataFrame column to centimeters
I trying to modify the value of just a single columns in a pandas dataframe, however, when I apply the map() function, another unrelated column also changes its values. How can I modify just a single column? columns = [[[776, 114, 67, 47], [776, 167, 67, 52], [776, 224, 67, 62],[776, 291, 67, 58],[776, 355,… Read More Map function modifies more than one column
Consider the following dataframe: column_of_lists scalar_col 0 [100, 200, 300] 100 1 [100, 200, 200] 200 2 [300, 500] 300 3 [100, 100] 200 The desired output would be a Series, representing how many times the scalar value of scalar_col appears inside the list column. So, in our case: 1 # 100 appears once in… Read More How to count the occurrences of a column's value in a column of lists?
`Following series, contains result as string of lists with values either PASS or FAIL. Input:- result "[‘PASS’,’FAIL’]" "[‘PASS’,’FAIL’,’PASS’,’FAIL’]" "[‘FAIL’,’FAIL’]" Output: result 1 1 0 If any row has at-least one PASS as value then return 1 else return 0 Input:- result "[‘PASS’,’FAIL’]" "[‘PASS’,’FAIL’,’PASS’,’FAIL’]" "[‘FAIL’,’FAIL’]" >Solution : If there are lists use in statement: df[‘result’] =… Read More Convert pandas series strings to numbers
I have this pd.Series: s = pd.Series([1, ‘a’, 1.4, ‘b’, 4, 98, 6.7, ‘hello’, 98.9]) My goal is to switch the values by each value type in reverse order. My desired output is: >>> s = pd.Series([98, ‘hello’, 98.9, ‘b’, 4, 1, 6.7, ‘a’, 1.4]) >>> s 0 98 1 hello 2 98.9 3 b… Read More Reverse values in pd.Series by their value type
I know what the where() method in pandas means. But in this case from the manual, I just cannot unterstand what they check with t = pd.Series([True, False]) Can someone maybe help? Here is the code: s = pd.Series(range(5)) t = pd.Series([True, False]) s.where(t, 99) 0 0 1 99 2 99 3 99 4 99… Read More Pandas: using where() method with another series?
I would like to know how to covert multiple series objects in list into a dataframe. import pandas as pd data = { "firstname": ["Sally", "Mary", "John"], "age": [50, 40, 30], "lastname" : ["a1", "b1", "c1"], } df = pd.DataFrame(data) tmp_list = list() for idx, row in df.iterrows(): tmp_list.append(row) My tmp_list looks like this. [firstname… Read More multiple series appended in a list using df.iterrows. How do I convert it into a dataframe?
I would like to change the format of the returned dates from this: listOfDates = df2[‘TradeDate’].drop_duplicates().reset_index(drop=True) print(listOfDates) 0 2022-02-02 1 2022-02-08 2 2022-05-01 3 2022-05-06 4 2022-06-05 5 2022-06-17 6 2022-07-30 7 2022-08-03 8 2022-10-10 9 2022-11-18 Name: TradeDate, dtype: datetime64[ns] to this: listOfDates = df2[‘TradeDate’].drop_duplicates().reset_index(drop=True) print(listOfDates) 0 20220202 1 20220208 2 20220501 3 20220506… Read More Changing the format of a column of data in a Pandas Series
I have a dataframe like as shown below customer_id revenue_m7 revenue_m8 revenue_m9 revenue_m10 1 1234 1231 1256 1239 2 5678 3425 3255 2345 I would like to do the below a) get average of revenue for each customer based on latest two columns (revenue_m9 and revenue_m10) b) get average of revenue for each customer based… Read More pandas average across dynamic number of columns