Without a minimal working example it is hard to provide you with clear recommendations, but I think what you are looking for is the .loc a pd.DataFrame. What I would recommend you doing is the following:
- Selection of rows with
.loc works better in your case if the dates are first converted to date-time, so a first step is to make this conversion as:
# Pandas is quite smart about guessing date format. If this fails, please check the
# documentation https://docs.python.org/3/library/datetime.html to learn more about
# format strings.
df['date'] = pd.to_datetime(df['date'])
# Make this the index of your data frame.
df.set_index('date', inplace=True)
- It is not clear how you intend to use conditionals/what is the content of your other columns. Using
.loc this is pretty straightforward
# At Feb 1, 2020, add a value to columns 'var'.
df.loc['2020-02-01', 'var'] = 0.727868
- This could also be used for ranges:
# Assuming you have a second `df2` which as a datetime columns 'date' with the
# data you wish to add to `df`. This will only work if all df2['date'] are found
# in df.index. You can workout the logic for your case.
df.loc[df2['date'], 'var2'] = df2['vals']
If the logic is to complex and the dataframe is not too large, iterating with .iterrows could be easier, specially if you are beginning with Python.
for idx, row in df.iterrows():
if idx in list_of_other_dates:
df.loc[i, 'var'] = (some code here)
Please clarify a bit your problem and you will get better answers. Do not forget to check the documentation.
