I'm using this code in order to groupby my data by year df = pd.read_csv('../input/companies-info-wikipedia-2021/sparql_2021-11-03_22-25-45Z.csv')
df = pd.read_csv('../input/companies-info-wikipedia-2021/sparql_2021-11-03_22-25-45Z.csv')
df_duplicate_name = df[df.duplicated(['name'])]
df = df.drop_duplicates(subset='name').reset_index()
df = df.drop(['a','type','index'],axis=1).reset_index()
df = df[~df['foundation'].str.contains('[A-Za-z]', na=False)]
df = df.drop([140,214,220])
df['foundation'] = df['foundation'].fillna(0)
df['foundation'] = pd.to_datetime(df['foundation'])
df['foundation'] = df['foundation'].dt.year
df = df.groupby('foundation')
But as a result it does not group it by foundation values:
0 0 Deutsche EuroShop AG 1999 http://dbpedia.org/resource/Germany Investment in shopping centers http://dbpedia.org/resource/Real_property 4 2.964E9 1.25E9 2.241E8 8.04E7
1 1 Industry of Machinery and Tractors 1996 http://dbpedia.org/resource/Belgrade http://dbpedia.org/resource/Tractors http://dbpedia.org/resource/Agribusiness 4 4.648E7 0.0 30000.0 -€0.47 million
2 2 TelexFree Inc. 2012 http://dbpedia.org/resource/Massachusetts 99 http://dbpedia.org/resource/Multi-level_marketing 7 did not disclose did not disclose did not disclose did not disclose
3 3 (prev. Common Cents Communications Inc.) 2012 http://dbpedia.org/resource/United_States 99 http://dbpedia.org/resource/Multi-level_marketing 7 did not disclose did not disclose did not disclose did not disclose
4 4 Bionor Holding AS 1993 http://dbpedia.org/resource/Oslo http://dbpedia.org/resource/Health_care http://dbpedia.org/resource/Biotechnology 18 NOK 253 395 million NOK 203 320 million 1.09499E8 NOK 49 020 million
... ... ... ... ... ... ... ... ... ... ... ...
255 255 Ageas SA/NV 1990 http://dbpedia.org/resource/Belgium http://dbpedia.org/resource/Insurance http://dbpedia.org/resource/Financial_services 45000 1.0872E11 1.348E10 1.112E10 9.792E8
256 256 Sharp Corporation 1912 http://dbpedia.org/resource/Japan Televisions, audiovisual, home appliances, inf... http://dbpedia.org/resource/Consumer_electronics 52876 NaN NaN NaN NaN
257 257 Erste Group Bank AG 2008 Vienna, Austria Retail and commercial banking, investment and ... http://dbpedia.org/resource/Financial_services 47230 2.71983E11 1.96E10 6.772E9 1187000.0
258 258 Manulife Financial Corporation 1887 200 Asset management, Commercial banking, Commerci... http://dbpedia.org/resource/Financial_services 34000 750300000000 47200000000 39000000000 4800000000
259 259 BP plc 1909 London, England, UK http://dbpedia.org/resource/Natural_gas http://dbpedia.org/resource/Petroleum_industry
I also tried with making it again pd.to_datetime and sorting by dt.year - but still unsuccessful.
Column names:
Index(['index', 'name', 'foundation', 'location', 'products', 'sector',
'employee', 'assets', 'equity', 'revenue', 'profit'],
dtype='object')
CodePudding user response：
I think you're misunderstanding how groupby() works.
You can't do df = df.groupby('foundation'). groupby() does not return a new DataFrame. Instead, it returns a GroupBy, which is essentially just a mapping from value grouped-by to a dataframe containg the rows that all share that value for the specified column.
You can, for example, print how many rows are in each group with the following code:
groups = df.groupby('foundation')
for val, sub_df in groups:
print(f'{val}: {sub_df.shape[0]} rows')
CodePudding user response：
@Ruslan you simply need to use a "sorting" command, not a "groupby" . You can achieve this generally in two ways:
myDF.sort_value(by='column_name' , ascending= 'true', inplace=true)
or, in case you need to set your column as index, you would need to do this:
myDF.index.name = 'column_name'
myDF.sort_index(ascending=True)
GroupBy is a totally different command, it is used to make actions after you group values by some criteria. Such as find sum, average , min, max of values, grouped-by some criteria.