pandas-groupby

问题 MCVE df = pd.DataFrame({ 'Cat': ['SF', 'W', 'F', 'R64', 'SF', 'F'], 'ID': [1, 1, 1, 2, 2, 2] }) df.Cat = pd.Categorical( df.Cat, categories=['R64', 'SF', 'F', 'W'], ordered=True) As you can see, I've define an ordered categorical column on Cat . To verify, check; 0 SF 1 W 2 F 3 R64 4 SF 5 F Name: Cat, dtype: category Categories (4, object): [R64 < SF < F < W] I want to find the largest category PER ID. Doing groupby + max works. df.groupby('ID').Cat.max() ID 1 W 2 F Name: Cat, dtype: object

问题 I try to create an output like this; I face difficulties to get my Total column. To get it I use merge as follows; Total = count*price column Here is my code; def generate_invoice_summary_info(): file_path = 'output.xlsx' df = pd.read_excel(file_path, sheet_name='Invoice Details', usecols="E:F,I,L:M") df['Price'] = df['Price'].astype(float) df1 = df.groupby(["Invoice Cost Centre", "Invoice Category"]).agg({'Price': 'sum'}).reset_index() df = pd.pivot_table(df, index=["Invoice Cost Centre",

问题 I have the following df >In [260]: df >Out[260]: size market vegetable confirm availability 0 Large ABC Tomato NaN 1 Large XYZ Tomato NaN 2 Small ABC Tomato NaN 3 Large ABC Onion NaN 4 Small ABC Onion NaN 5 Small XYZ Onion NaN 6 Small XYZ Onion NaN 7 Small XYZ Cabbage NaN 8 Large XYZ Cabbage NaN 9 Small ABC Cabbage NaN 1) How to get the size of a vegetable whose size count is maximum? I used groupby on vegetable and size to get the following df But I need to get the rows which contain the

问题 Is there a pythonic way to group by a field and check if all elements of each resulting group have the same value? Sample data: datetime rating signal 0 2018-12-27 11:33:00 IG 0 1 2018-12-27 11:33:00 HY -1 2 2018-12-27 11:49:00 IG 0 3 2018-12-27 11:49:00 HY -1 4 2018-12-27 12:00:00 IG 0 5 2018-12-27 12:00:00 HY -1 6 2018-12-27 12:49:00 IG 0 7 2018-12-27 12:49:00 HY -1 8 2018-12-27 14:56:00 IG 0 9 2018-12-27 14:56:00 HY -1 10 2018-12-27 15:12:00 IG 0 11 2018-12-27 15:12:00 HY -1 12 2018-12-20

问题 Is there a pythonic way to group by a field and check if all elements of each resulting group have the same value? Sample data: datetime rating signal 0 2018-12-27 11:33:00 IG 0 1 2018-12-27 11:33:00 HY -1 2 2018-12-27 11:49:00 IG 0 3 2018-12-27 11:49:00 HY -1 4 2018-12-27 12:00:00 IG 0 5 2018-12-27 12:00:00 HY -1 6 2018-12-27 12:49:00 IG 0 7 2018-12-27 12:49:00 HY -1 8 2018-12-27 14:56:00 IG 0 9 2018-12-27 14:56:00 HY -1 10 2018-12-27 15:12:00 IG 0 11 2018-12-27 15:12:00 HY -1 12 2018-12-20

问题 I have a dataframe that looks like this: from to datetime other ------------------------------------------------- 11 1 2016-11-06 22:00:00 - 11 1 2016-11-06 20:00:00 - 11 1 2016-11-06 15:45:00 - 11 12 2016-11-06 15:00:00 - 11 1 2016-11-06 12:00:00 - 11 18 2016-11-05 10:00:00 - 11 12 2016-11-05 10:00:00 - 12 1 2016-10-05 10:00:59 - 12 3 2016-09-06 10:00:34 - I want to groupby "from" and then "to" columns and then sort the "datetime" in descending order and then finally want to calculate the

问题 I have dates in Python (pandas) written as "1/31/2010". To apply linear regression I want to have 3 separate variables: number of day, number of month, number of year. What will be the way to split a column with date in pandas into 3 columns? Another question is to have the same but group days into 3 groups: 1-10, 11-20, 21-31. 回答1: df['date'] = pd.to_datetime(df['date']) #Create 3 additional columns df['day'] = df['date'].dt.day df['month'] = df['date'].dt.month df['year'] = df['date'].dt

问题 I want to group by one column (tag) and sum up the corresponding quantites (qty). The related reference no. column should be separated by commas import pandas as pd tag = ['PO_001045M100960','PO_001045M100960','PO_001045MSP2526','PO_001045M870191', 'PO_001045M870191', 'PO_001045M870191'] reference= ['PA_000003', 'PA_000005', 'PA_000001', 'PA_000002', 'PA_000004', 'PA_000009'] qty=[4,2,2,1,1,1] df = pd.DataFrame({'tag' : tag, 'reference':reference, 'qty':qty}) tag reference qty PO

问题 This question has been solved with R, but I haven't seen useful examples with Python. I would like to learn how to convert calendar year (1/1/1990 to 12/31/2010) discharge data to water year data (i.e. 10/01/1990 to 9/31/2010). Thank you for the assistance. 回答1: You could use apply and write your own function to create a new column WY : IF you have have df : Date Discharge 0 2011-10-01 00:00:00 0.0 1 2011-10-01 01:00:00 0.0 2 2011-10-01 02:00:00 0.0 3 2011-10-01 03:00:00 0.0 4 2011-10-01 04

问题 I have data which looks like this: Group string A Hello A SearchListing A GoSearch A pen A Hello B Real-Estate B Access B Denied B Group B Group C Glance C NoSearch C Home and so on I want to find out all those group who have "search" phrase in the strings and mark them as 0/1. At the same time I want to aggregate results like unique strings and total strings with respect to each group and also, how many times "search" was encountered by that group. The end results which I want is something