dataframe

问题 I am having below pandas dataframe df . I am trying to rename the column names but it not working as expected. Code: mapping = {df.columns[0]:'Date', df.columns[1]: 'A', df.columns[2]:'B', df.columns[3]: 'C',df.columns[4]:'D', df.columns[5]: 'E',df.columns[6]:'F', df.columns[7]: 'G',df.columns[8]:'H', df.columns[9]: 'J'} df.rename(columns=mapping) Output of df.columns : MultiIndex(levels=[['A Index', 'B Index', 'C Index', 'D Index', 'E Index', 'F Index', 'G Index', 'H Index', 'I Index', 'J

问题 I am having below pandas dataframe df . I am trying to rename the column names but it not working as expected. Code: mapping = {df.columns[0]:'Date', df.columns[1]: 'A', df.columns[2]:'B', df.columns[3]: 'C',df.columns[4]:'D', df.columns[5]: 'E',df.columns[6]:'F', df.columns[7]: 'G',df.columns[8]:'H', df.columns[9]: 'J'} df.rename(columns=mapping) Output of df.columns : MultiIndex(levels=[['A Index', 'B Index', 'C Index', 'D Index', 'E Index', 'F Index', 'G Index', 'H Index', 'I Index', 'J

问题 I want to find the way change name of specific column in a multilevel dataframe. With this data: data = { ('A', '1', 'I'): [1, 2, 3, 4, 5], ('B', '2', 'II'): [1, 2, 3, 4, 5], ('C', '3', 'I'): [1, 2, 3, 4, 5], ('D', '4', 'II'): [1, 2, 3, 4, 5], ('E', '5', 'III'): [1, 2, 3, 4, 5], } dataDF = pd.DataFrame(data) This code not working: dataDF.rename(columns = {('A', '1', 'I'):('Z', '100', 'Z')}, inplace=True) Result: A B C D E 1 2 3 4 5 I II I II III 0 1 1 1 1 1 1 2 2 2 2 2 2 3 3 3 3 3 3 4 4 4 4 4

问题 Is there any way I can retain the original index of my large dataframe after I perform a groupby? The reason I need to this is because I need to do an inner merge back to my original df (after my groupby) to regain those lost columns. And the index value is the only 'unique' column to perform the merge back into. Does anyone know how I can achieve this? My DataFrame is quite large. My groupby looks like this: df.groupby(['col1', 'col2']).agg({'col3': 'count'}).reset_index() This drops my

问题 Is there any way I can retain the original index of my large dataframe after I perform a groupby? The reason I need to this is because I need to do an inner merge back to my original df (after my groupby) to regain those lost columns. And the index value is the only 'unique' column to perform the merge back into. Does anyone know how I can achieve this? My DataFrame is quite large. My groupby looks like this: df.groupby(['col1', 'col2']).agg({'col3': 'count'}).reset_index() This drops my

问题 I'd like to know if it's possible to display a pandas dataframe in VS Code while debugging (first picture) as it is displayed in PyCharm (second picture) ? Thanks for any help. df print in vs code: df print in pycharm: 回答1: As of the January 2021 release of the python extension, you can now view pandas dataframes with the built-in data viewer when debugging native python programs. When the program is halted at a breakpoint, right-click the dataframe variable in the variables list and select

问题 I'd like to know if it's possible to display a pandas dataframe in VS Code while debugging (first picture) as it is displayed in PyCharm (second picture) ? Thanks for any help. df print in vs code: df print in pycharm: 回答1: As of the January 2021 release of the python extension, you can now view pandas dataframes with the built-in data viewer when debugging native python programs. When the program is halted at a breakpoint, right-click the dataframe variable in the variables list and select

问题 I am currently working on implement the C# version of a Gurobi linear program model that was earlier built in Python. I have a number of CSV files from which I was importing the data and creating pandas dataframes, and I was fetching columns from those dataframes to create variables that I was using in my Linear Program. The python code for creating the variables using dataframes is as follows: dataPath = "C:/Users/XYZ/Desktop/LinearProgramming/TestData" routeData = pd.DataFrame.from_csv(os

问题 I am currently working on implement the C# version of a Gurobi linear program model that was earlier built in Python. I have a number of CSV files from which I was importing the data and creating pandas dataframes, and I was fetching columns from those dataframes to create variables that I was using in my Linear Program. The python code for creating the variables using dataframes is as follows: dataPath = "C:/Users/XYZ/Desktop/LinearProgramming/TestData" routeData = pd.DataFrame.from_csv(os

问题 I'm trying to Run a simulation but I'm having trouble storing multiple dataframes called "data_i" in a list ordering by i. I start with a df called "data_", which has data from 1901 to 2032 (132 rows). I apply a loop to create one dataframe per row called data_1, data_2,data_3,...,data_132 (row of 2032 is stored in data_132). Finally, I store all this dataframes in a list and use lapply to create a column in each dataframe. Here is a reproducible example: #Main dataframe time <- 1901:2032 b <