pandas

问题 I want my dataframe to display the first row names as my dataframe column name instead of numbering from 0 etc. How do I do this? I tried using pandas and openpyxl modules to turn my Excel spreadsheet into a dataframe. import pandas as pd from openpyxl import load_workbook from openpyxl.utils.dataframe import dataframe_to_rows wb = load_workbook(filename='Budget1.xlsx') print(wb.sheetnames) sheet_ranges=wb['May 2019'] print(sheet_ranges['A3'].value) ws=wb['May 2019'] df=pd.DataFrame(ws.values

问题 I want my dataframe to display the first row names as my dataframe column name instead of numbering from 0 etc. How do I do this? I tried using pandas and openpyxl modules to turn my Excel spreadsheet into a dataframe. import pandas as pd from openpyxl import load_workbook from openpyxl.utils.dataframe import dataframe_to_rows wb = load_workbook(filename='Budget1.xlsx') print(wb.sheetnames) sheet_ranges=wb['May 2019'] print(sheet_ranges['A3'].value) ws=wb['May 2019'] df=pd.DataFrame(ws.values

问题 This question already has answers here : Changing multiple column names but not all of them - Pandas Python (4 answers) Closed 1 year ago . I am trying to rename selected columns (say the two las columns) in my data frame using the iloc and df.columns functions but it does not seem to work for me and I can't figure out why. Here is a toy example of what I want to achieve: import pandas as pd d = {'one': list(range(5)), 'two': list(range(5)), 'three': list(range(5)), 'four': list(range(5)),

问题 This should be fairly simple but have not been able to wrap my brain around it. I am trying to convert df1 to df2, where df1 and df2 are pandas dataframes df1 = pd.DataFrame({'site': ['1', '2'], 'sat_open': ['0900', '0900'], 'sat_close': ['1900','1900'], 'sun_open': ['1000', '1000'], 'sun_close': ['1800', '1800'], 'mon_open': ['0900', '0900'], 'mon_close': ['2100', '2100'] }) df2 = pd.DataFrame({'store': ['1', '1', '1', '2', '2','2'], 'day': ['sat', 'sun', 'mon','sat', 'sun', 'mon'], 'open':

问题 This should be fairly simple but have not been able to wrap my brain around it. I am trying to convert df1 to df2, where df1 and df2 are pandas dataframes df1 = pd.DataFrame({'site': ['1', '2'], 'sat_open': ['0900', '0900'], 'sat_close': ['1900','1900'], 'sun_open': ['1000', '1000'], 'sun_close': ['1800', '1800'], 'mon_open': ['0900', '0900'], 'mon_close': ['2100', '2100'] }) df2 = pd.DataFrame({'store': ['1', '1', '1', '2', '2','2'], 'day': ['sat', 'sun', 'mon','sat', 'sun', 'mon'], 'open':

问题 i have a text file with continuous data. The following text file contains 2 lines Example: 123@#{} 456@$% 1 23 Also, I have column lengths given as 2,3,4 for 3 columns that i need in my data frame. I want to parse the file into a pandas data frame such that the first column gets the first 2 letters, the second column gets the next 3 letters and so on as per the column lengths given (2,3,4).. the next set of letters should form the next row and so on... so my pandas data frame should look like

问题 I was not clear about my issue, so I am reviewing the question. I have a function manipulating a generic dataframe (it removes and renames columns and records): def manipulate_df(df_local): df_local.rename(columns={'A': 'grouping_column'}, inplace = True) df_local.drop('B', axis=1, inplace=True) df_local.drop(df.query('grouping_column not in (\'1\', \'0\')').index, inplace = True) df_local = df_local.groupby(['grouping_column'])['C'].sum().to_frame().reset_index().copy() print("this is what I

问题 I was not clear about my issue, so I am reviewing the question. I have a function manipulating a generic dataframe (it removes and renames columns and records): def manipulate_df(df_local): df_local.rename(columns={'A': 'grouping_column'}, inplace = True) df_local.drop('B', axis=1, inplace=True) df_local.drop(df.query('grouping_column not in (\'1\', \'0\')').index, inplace = True) df_local = df_local.groupby(['grouping_column'])['C'].sum().to_frame().reset_index().copy() print("this is what I

问题 I am new to ML and Data Science (recently graduated from Master's in Business Analytics) and learning as much as I can by myself now while looking for positions in Data Science / Business Analytics. I am working on my personal project to build ML algorithms to predict if a customer will show up to their existing appointment. Upon initial data analysis, I notice that my "No-show" column contains values "Yes" and "No" (Metadata: if a customer scheduled an appointment and showed up for an

问题 When fitting a statsmodel, I'm receiving a warning about the date frequency. First, I import a dataset: import statsmodels as sm df = sm.datasets.get_rdataset(package='datasets', dataname='airquality').data df['Year'] = 1973 df['Date'] = pd.to_datetime(df[['Year', 'Month', 'Day']]) df.drop(columns=['Year', 'Month', 'Day'], inplace=True) df.set_index('Date', inplace=True, drop=True) Next I try to fit a SES model: fit = sm.tsa.api.SimpleExpSmoothing(df['Wind']).fit() Which returns this warning: