I have the data of pandas dataframe.
for i,row in stock.iterrows():
pprint(row)
it returns data like this
Data 3.25000
Name: 2000-01-03 00:00:00, dtype: float64
I can access 3.25000 as row['Data'], however row['Name'] is unavailable.
How can I access 2000-01-03 00:00:00 ???
I have used dataframe with index.
Name means the same as index???
I think Name is index name.
You need i for index values:
for i,row in stock.iterrows():
pprint(row)
#index
pprint(i)
Sample:
stock = pd.DataFrame({'Data':[3.5, 5.6]}, index=pd.date_range('2000-01-03', periods=2))
stock.index.name = 'Name'
print (stock)
Data
Name
2000-01-03 3.5
2000-01-04 5.6
print (stock.index.name)
Name
print (stock.index)
DatetimeIndex(['2000-01-03', '2000-01-04'], dtype='datetime64[ns]', name='Name', freq='D')
for i,row in stock.iterrows():
pprint(row)
#index
pprint(i)
Data 3.5
Name: 2000-01-03 00:00:00, dtype: float64
Timestamp('2000-01-03 00:00:00', freq='D')
Data 5.6
Name: 2000-01-04 00:00:00, dtype: float64
Timestamp('2000-01-04 00:00:00', freq='D')
index name is converted to column name after reset_index:
stock = stock.reset_index()
print (stock)
Name Data
0 2000-01-03 3.5
1 2000-01-04 5.6
print (stock.index.name)
None
print (stock.index)
RangeIndex(start=0, stop=2, step=1)
print (stock.columns)
Index(['Name', 'Data'], dtype='object')
