Python Pandas dataframe - 根据索引值添加新列

Question

I have a Python pandas dataframe that looks like this:

      year_2000  year_1999  year_1998  year_1997  year_1996  year_1995 (MANH, stock name)
MANH 454.47     -71.90     nan        nan        nan        nan         TEST             
LH   385.52     180.95     -24.14     -41.67     -68.92     -26.47      TEST             
DGX  373.33     68.04      4.01       nan        nan        nan         TEST             
SKX  306.56     nan        nan        nan        nan        nan         TEST 

where the stock tickers are the index. I want to add the name of each stock as a new column

I tried adding the stock name column via yearly_best['MANH','stock name']='TEST' but it adds the same name in all rows.

I have a dictionary called ticker_name which contains the tickers and the names

Out[65]: 
{'TWOU': '2U',
 'MMM': '3M',
 'ABT': 'Abbott Laboratories',
 'ABBV': 'AbbVie Inc.',
 'ABMD': 'Abiomed',
 'ACHC': 'Acadia Healthcare',
 'ACN': 'Accenture',
 'ATVI': 'Activision Blizzard',
 'AYI': 'Acuity Brands',
 'ADNT': 'Adient',

thus I would like to get the names from the dict and put then in a column in the dataframe. How can I do that?

Answer 1

As the key of your dictionnary are index of your dataFrame you can try:

d = {'TWOU': '2U',
'MMM': '3M',
'ABT': 'Abbott Laboratories',
'ABBV': 'AbbVie Inc.',
'ABMD': 'Abiomed',
'ACHC': 'Acadia Healthcare',
'ACN': 'Accenture',
'ATVI': 'Activision Blizzard',
'AYI': 'Acuity Brands',
'ADNT': 'Adient',}

df['stock name'] = pd.Series(d)

Answer 2

You can try:

# Create a new column "stock_name" with the index values
yearly_best['stock_name'] = yearly_best.index
# Replace the "stock_name" values based on the dictionary
yearly_best['stock_name'].map(ticker_name, inplace=True)

Note that in this case, the dataframe's indices will remain as they were (stock tickers). If you would like to replace the indices with row numbers, consider using reset_index