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