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 NaN NaN 3 4 1 2.0 3.0 4.0 NaN NaN 4 6 1 2.0 3.0 … Read more
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 it’s own complete structure: [{‘name’:i[0], ‘children’: [{‘name’: i[1], ‘value’: i[2]}]} for i in tuples] But … Read more
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( {‘id’: [1, 2, 2, 2, 2, 2, 1, 1, 1, 1, 1, 1, 1, 1, … Read more
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, “/usr/lib/python2.7/site-packages”) This line contains two statements. Split them into two lines: import pandas as pd sys.path.insert(0, “/usr/lib/python2.7/site-packages”) Or, if they must be in one line, separate them with semicolon (highly not recomended!!!): import pandas as pd; sys.path.insert(0, “/usr/lib/python2.7/site-packages”) 1 solved Python returns “SyntaxError: invalid syntax sys module” [closed]
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 4 4 4 2 solved converting a text file to csv file with out index
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): rating to be validated Returns: True if rating is valid False if rating is not … Read more
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”] Let’s get the first five nations. >>> ratio.head() Recovered Confirmed Ratio Country_Region Afghanistan 41727 52513 … Read more
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, put the timestamp part into a ‘time’ column. In order to get times between 9 … Read more
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 how to save sql query result to csv in pandas
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 I loop to get the list named vlist?
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 = [ [11238,3943,201805030759165986,’新建订单’,20180503075916,’2018/5/3 07:59:16′,’2018/5/3 07:59:16′], [11239,3943,201805030759165986,’新建订单’,20180503082115,’2018/5/3 08:21:15′,’2018/5/3 08:21:15′], [11240,3943,201805030759165986,’新建订单’,20180503083204,’2018/5/3 08:32:04′,’2018/5/3 08:32:04′], [11241,3941,201805030856445991,’新建订单’,20180503085644,’2018/5/3 08:56:02′,’2018/5/3 08:56:44′], [11242,3941,201805022232081084,’初审成功’,20180503085802,’2018/5/3 08:58:02′,’2018/5/3 08:58:02′], … Read more