pandas

问题 I have a dataframe like below :- id creTimestamp CPULoad instnceId 0 2021-01-22 18:00:00 22.0 instanceA 1 2021-01-22 19:00:00 22.5 instanceA 2 2021-01-22 20:00:00 23.5 instanceA 3 2021-01-22 18:00:00 24.0 instanceB 4 2021-01-22 19:00:00 24.5 instanceB 5 2021-01-22 20:00:00 22.5 instanceB 6 2021-01-24 18:00:00 23.0 instanceA 7 2021-01-24 19:00:00 23.5 instanceA 8 2021-01-24 20:00:00 24.0 instanceA 9 2021-01-24 18:00:00 25.5 instanceB 10 2021-01-24 19:00:00 28.5 instanceB 11 2021-01-24 20:00:00

问题 I'm using Colab to run the following code: import numpy as np import pandas as pd MAP_locs = ["LOAD POINT 1","LOAD POINT 2","DELIVERY POINT"] MAP_SIZE = len(MAP_locs) LOAD_POINT_1 = [] LOAD_POINT_2 = [] DELIVERY_POINT = [] for i in range(10): LOAD_POINT_1.append(0.5) LOAD_POINT_2.append(0.5) DELIVERY_POINT.append(-1) d = {'LOAD POINT 1': LOAD_POINT_1, 'LOAD POINT 2': LOAD_POINT_2, 'DELIVERY POINT': DELIVERY_POINT} df = pd.DataFrame(data=d) VESSEL_Y = 6 VESSEL_X = [1,0,0] VESSEL_X_to_df = MAP

问题 Normal syntax for calling a function is func() but I have noticed that loc[] in pandas is without parentheses and still treated as a function. Is loc [] really a function in pandas? 回答1: Is loc[ ] a function in Pandas? No. The simplest way to check is: import pandas as pd df = pd.DataFrame() print(df.loc.__class__) which prints <class 'pandas.core.indexing._LocIndexer'> this tells us that df.loc is an instance of a _LocIndexer class. The syntax loc[] derives from the fact that _LocIndexer

问题 I have a live stock API for ML implementation i'm using Google colab as the environment and i have established contact with API and the status is success and i have received a json text file and i have parsed as a .json() and make as viewable and indented. { "Meta Data": { "1. Information": "Daily Prices and Volumes for Digital Currency", "2. Digital Currency Code": "BTC", "3. Digital Currency Name": "Bitcoin", "4. Market Code": "CNY", "5. Market Name": "Chinese Yuan", "6. Last Refreshed":

问题 I have a long format dataframe like the following: In reality, my dataframe is about 10k rows and there are about 400 possible values of category. I'd like to melt my dataframe into a wide format like the following: I can think of a couple ways to do this, but what would be an efficient way to do this transformation? 回答1: Check with crosstab out = pd.crosstab(df['Obs'], df['Category']) 回答2: Isn't this crosstab: pd.crosstab(df['Obs'], df['Category']) 来源： https://stackoverflow.com/questions

问题 When I try to click on data frames in the Variable Explorer in Spyder, I get the following error: This happens with all data frames (I tried making my own). As a side note, functions like numpy.sum and .describe() don't output anything, but I'm unsure if this is related to the KeyError. Thank you! 回答1: Add pandas to the packages option in setup.py module. Here is the code snippet for setup.py from setuptools import setup APP = ['automating_finances0.py'] DATA_FILES = [] OPTIONS = {'packages':

问题 I have a dataframe of user connections where UID represents a user, and date represents the date on which the user made connections (represented by #fans). UID Date #fans 9305 1/25/2015 5 9305 2/26/2015 7 9305 3/27/2015 8 9305 4/1/2015 9 1305 6/6/2015 14 1305 6/26/2015 16 1305 6/27/2015 17 The date range of the dataframe is 01-01-2014 to 12-01-2020. I need to expand the data such that for each user the date should contain each date in the date range and each date should have #fans as total

问题 I have the following data frame: import pandas as pd df = pd.DataFrame({'Manufacturer':['Allen Edmonds', 'Louis Vuitton 23', 'Louis Vuitton 8', 'Gulfstream', 'Bombardier', '23 - Louis Vuitton', 'Louis Vuitton 20'], 'System':['None', 'None', '14 Platinum', 'Gold', 'None', 'Platinum 905', 'None'] }) I would like to create another column in the data frame named Pricing , which contains the value "East Coast" if the following conditions hold: a) if a substring in the Manufacturer column matches

问题 In Pandas, I have a dataframe with ZipCode, Age, and a bunch of columns that should all have values 1 or 0, ie: ZipCode Age A B C D 12345 21 0 1 1 1 12345 22 1 0 1 4 23456 45 1 0 1 1 23456 21 3 1 0 0 I want to delete all rows in which 0 or 1 doesn't appear in columns A,B,C, or D as a way to clean up the data. In this case, I would remove the 2nd and 4th row because 4 appears in column D in row 2 and 3 appears in column A in row 4. I want to do this even if I have 100 columns to check such

问题 In Pandas, I have a dataframe with ZipCode, Age, and a bunch of columns that should all have values 1 or 0, ie: ZipCode Age A B C D 12345 21 0 1 1 1 12345 22 1 0 1 4 23456 45 1 0 1 1 23456 21 3 1 0 0 I want to delete all rows in which 0 or 1 doesn't appear in columns A,B,C, or D as a way to clean up the data. In this case, I would remove the 2nd and 4th row because 4 appears in column D in row 2 and 3 appears in column A in row 4. I want to do this even if I have 100 columns to check such