df.loc[((df.SPIN == ‘COR’) | (df.SPIN == r’F/R’)), ‘MTM’] = (df[“MARKET PRICE”] – df[“TRADE PRICE”]) * ( df[“QTY”] / 100) df.rename(columns={‘MTM’: ‘Market to Market’}, inplace=True) reportName = “Output” reportFileOut = rptDirPath + ‘\\’ + reportName + ‘.xlsx’ writer = pd.ExcelWriter(reportFileOut) df.to_excel(writer, ‘Sheet1’) writer.save()
Yes, there is the split function, however before calling it on your string, you must get rid of the [ and ], or else instead of [‘1’, ‘2’, ‘3’, ‘4’], you will get [‘[1’, ‘2’, ‘3’, ‘4]’], so instead of s.split(), we do s[1:-1].split(), also this means your list is strings instead of ints (‘1’ … Read more
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)]
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()
Try this: b[‘col3’] = (b[‘col1’] + ‘-‘ + b[‘col2’]) print(b.groupby(‘col3’).size()) Output: a-b 2 b-a 2 c-d 1 d-c 1 EDIT 1 Based on your input data (as in comments), here is the df I made up and the results Code: df[[‘seller_city’,’customer_city’]] Output: seller_city customer_city 0 volta redonda sao jose dos pinhais 1 volta redonda sao … Read more
You can simply use the csv module from the standard library: import csv with open(‘__.csv’, ‘r’, newline=””) as f: reader = csv.reader(f) _ , *header = next(reader) d = {} for k, *row in reader: d[k] = dict(zip(header, row)) print(d) {‘reviews’: {‘JOURNAL_IMPACT_FACTOR’: 27.324, ‘IMPACT_FACTOR_LABEL’: ‘Highest’, ‘IMPACT_FACTOR_WEIGHT’: 37.62548387}, ‘hairdoos’: {‘JOURNAL_IMPACT_FACTOR’: 40.0, ‘IMPACT_FACTOR_LABEL’: ‘middle’, ‘IMPACT_FACTOR_WEIGHT’: 50.0}, ‘skidoos’: … Read more
The blank grey row that you’ve pointed out is there to make room for the name of the DataFrame’s index, which is variety. That index came from the default behaviour of df.groupby(): the grouped-by column(s) end up in the index of the resulting DataFrame. To override this, try df.groupby(‘variety’, as_index=False). Or, if you have a … Read more
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
If you only want groups of two (as opposed to groups of n), then you can hardcode n=2 and use a list comprehension to return a list of lists. This will also create a group of one at the end of the list if the length of the list is odd: some_list = [‘a’,’b’,’c’,’d’,’e’] [some_list[i:i+2] … Read more
Change the import Pandas to import pandas as pd