Try this: (df.join(pd.DataFrame( df[‘number’] .map(lambda x: range(1,x+1)).tolist()) .rename(lambda x: ‘C{}’.format(x+1),axis=1))) Output: number C1 C2 C3 C4 C5 C6 0 1 1 NaN NaN NaN NaN NaN 1 2 1 2.0 NaN NaN NaN NaN 2 3 1 2.0 3.0 NaN…
Assuming you want a list of dicts as the output of the json with each dict be the form in your question: The following one liner will put it into the data structure your looking for with each tuple generating…
From your expected result, it appears that each “group” is based on contiguous id values. For this, you can use the compare-cumsum-groupby pattern, and then use agg to get the min and max values. # Sample data. df = pd.DataFrame(…
If your second dataframe in your upper screenshot is called df, this should do: df.set_index(‘Name’).stack().reset_index(level=0) 2 solved Panda Data Manipulation
temp = df.groupby(‘date’)[‘t’].cumsum().rename(‘t_cumulative’) df = df.merge(temp, on=’date_time’, how=’outer’) solved Calculate cumulative intraday measures that reset every day in pandas
First, move the non-date columns in to the index, then use replace and reset_index: df = df.set_index(‘CashFlows’) df = df.rename(columns=lambda x: ‘year_’+str(x.year)) solved How to change column names from numbers to names [closed]
import pandas as pd sys.path.insert(0, “”) This line contains two statements. Split them into two lines: import pandas as pd sys.path.insert(0, “”) Or, if they must be in one line, separate them with semicolon (highly not recomended!!!): import pandas as…
Try this: from io import StringIO csvtext = StringIO(“””1 1 1 1 1 2 3 4 4 4 4″””) data = pd.read_csv(csvtext, sep=’\n’, header=None) data.to_csv(‘out.csv’, index=False, header=False) #on windows !type out.csv Output: 1 1 1 1 1 2 3 4…
After going through the Pandas documentation, found the way to validate the data. Let’s just say you have a custom validation function. def validate_rating(rating): “””” Description: validate if hotel rating is a digit between 0 to 5 Args: rating (str):…
Computing the ratio Since there are multiple regions in a country, there are duplicated values in the Country_Region column. Therefore, I use groupby to sum the total cases of a nation. ratio = df.groupby(“Country_Region”)[[“Recovered”, “Confirmed”]].sum() ratio[“Ratio”] = ratio[“Recovered”] / ratio[“Confirmed”]…
after converting to datetime pd.to_datetime(df[‘date’]) you can create a separate column with the hour in it, e.g. df[‘Hour’] = df.date.dt.hour and then sort by it df.sort_values(‘Hour’) EDIT: As you want to sort by time you can instead of using hour,…
You can try following code: import pandas as pd df1 = pd.read_csv(“Insert file path”) df2 = pd.read_csv(“Insert file path”) df1[‘Date’] = pd.to_datetime(df1[‘Date’] ,errors=”coerce”,format=”%Y-%m-%d”) df2[‘Date’] = pd.to_datetime(df2[‘Date’] ,errors=”coerce”,format=”%Y-%m-%d”) df = df1.merge(df2,how=’inner’, on =’Date’) df.to_csv(‘data.csv’,index=False) This should solve your problem. 4 solved…
Check this out. List Comprehension import numpy as np import pandas as pd df = pd.read_csv(‘census.csv’) data = [‘SUMLEV’,’STNAME’, ‘CTYNAME’, ‘CENSUS2010POP’] df=df[data] adf = df[df[‘SUMLEV’]==50] adf.set_index(‘STNAME’, inplace=True) states = np.array(adf.index.unique()) vlist=[adf.loc[states[i]][‘CENSUS2010POP’].sort_values(ascending =False).head(3).sum() for i in range(0,7)] 3 solved how can…
You can try one of the 2 alternatives below. df.groupby(‘country’)[‘edition’].nunique().gt(1).sum() or (df.groupby(‘country’)[‘edition’].nunique()>(1)).sum() 1 solved how many countries have organised more than one edition of olympic games? using python pandas [closed]
I think I understand what you’re asking. You just want to have a new dataframe that calculates the time difference between the three different entries for each unique order id? So, I start by creating the dataframe: data = […