First Date column not there when using info()-CodePudding

I got data from yahoo finance and somehow the first date column is there when i just look at the data:

                            Open        High        Low         Close       Volume      Dividends   Stock Splits
Date                            
2015-01-02 00:00:00-05:00   40.724155   41.387470   40.619421   40.811432   27913900    0.0 0.0
2015-01-05 00:00:00-05:00   40.471039   40.785243   40.366306   40.436131   39673900    0.0 0.0
2015-01-06 00:00:00-05:00   40.479777   40.802706   39.746637   39.842644   36447900    0.0 0.0
2015-01-07 00:00:00-05:00   40.130662   40.549598   39.702999   40.348858   29114100    0.0 0.0
2015-01-08 00:00:00-05:00   40.802692   41.675477   40.776510   41.535831   29645200    0.0 0.0

but not there when in use info():

<class 'pandas.core.frame.DataFrame'>
DatetimeIndex: 1257 entries, 2015-01-02 00:00:00-05:00 to 2019-12-30 00:00:00-05:00
Data columns (total 7 columns):
 #   Column        Non-Null Count  Dtype  
---  ------        --------------  -----  
 0   Open          1257 non-null   float64
 1   High          1257 non-null   float64
 2   Low           1257 non-null   float64
 3   Close         1257 non-null   float64
 4   Volume        1257 non-null   int64  
 5   Dividends     1257 non-null   float64
 6   Stock Splits  1257 non-null   float64
dtypes: float64(6), int64(1)
memory usage: 110.9 KB

Is there a way to get it there and define it as datetime64[ns]

this is the code for getting the data from yahoo:

company_name = "MSFT"

company = tweets[tweets['ticker_symbol'] == company_name]
company_stock =  yf.Ticker(company_name).history(start=min(company.date).date(),end=max(company.date).date())

Thank you in advance

I hope I can get the date into the dataframe and change it as datetime[ns]

CodePudding user response：

That date value is not a column in the dataframe.The dataframe is currently indexed by date.

import yfinance as yf
company_name = "MSFT"
df = yf.Ticker(company_name).history()
print(list(df.columns))
# ['Open', 'High', 'Low', 'Close', 'Volume', 'Dividends', 'Stock Splits']

You can add the date index as a new column in the dataframe.

df['date'] = df.index
print(list(df.columns))
# ['Open', 'High', 'Low', 'Close', 'Volume', 'Dividends', 'Stock Splits', 'date']

The new date column is in datetime64[ns] format:

print(df.dtypes['date'])
# datetime64[ns]
# or to see all dtypes: print(df.dtypes)