I have two dataframes like this:
result dataframe is large and contains COMPANY names from bse dataframe but TOKEN and SYMBOL information is missing. I want to merge result dataframe with bse dataframe so that TOKEN and SYMBOL info of the companies in bse dataframe are added to the result dataframe.
When I merge, it duplicates the columns and rename them like this:
Problem is more like 'fill in the blanks'. I need to fill the TOKEN and SYMBOL information from bse dataframe into the corresponding rows of result dataframe.
I want to avoid renaming and duplication.
Can anyone help?
thanks in advance.
Related
I have a data frame with mutliple columns and some of these have missing values.
I would to filter so that I can return a dataframe that has missing values on one or two specific columns.
Can anyone help me figure out how to do that?
Having a dataframe "df" with a columns "A"
df_missing = df[df['A'].isnull()]
Below i have a pandas dataframe having multiindex, one level is "Address" and second level is (score,num,axis).All total one column.
Output i am looking as separate columns under 'Address' column index.
print(df.columns.tolist())
The question is not exactly clear but from what i got, try this:
df.reset_index().set_index('Address')
Consider I have a dataframe with 2 columns: the first column is 'Name' in the form of a string and the second is 'score' in type int. There are many duplicate Names and they are sorted such that the all 'Name1's will be in consecutive rows, followed by 'Name2', and so on. Each row may contain a different score.The number of duplicate names may also be different for each unique string.'
I wish to extract data afrom this dataframe and put it in a new dataframe such that There are no duplicate names in the name column, and each name's corresponding score is the average of his scores in the original dataframe.
I've provided a picture for a better visualization:
Firstly make use of groupby() method as mentioned by #QuangHong:
result=df.groupby('Name', as_index=False)['Score'].mean()
Finally make use of rename() method:
result=result.rename(columns={'Score':'Avg Score'})
I have a dataframe which has 2 columns named TABLEID and STATID
There are different values in the both the columns.
when I filter the dataframe on values say '101PC' and 'ST101', it gives me 14K records and when I filter the dataframe on values say '102HT' and 'ST102', it gives me 14K records also. The issue is when I try to combine both the filters like below it gives me blank dataframe. I was expecting 28K records in my resultant dataframe. Any help is much appreciated
df[df[['TABLEID','STATID']].apply(tuple, axis = 1).isin([('101PC', 'ST101'), ('102HT','ST102')])]
I have two dataframes, dframe and dframp. Dframe has 301497 rows in it and dframep has 6080 rows in it. Both dataframes are show below. I want to merge the two such that when dframep is added to dframe the new dataframe puts Nans where dframep does not have any values for that date. I have tried this:
dfall = dframe.merge(dframep, on=['ID','date','instrument','close'], how='outer')
The two merge together but the result is 307577 rows e.g. for the dates that are not in dframep there are no Nan's.
Pulling my hair out so any help would be appreciated. Im guessing it has something to do with indexing and selecting the columns correctly.
Thanks
I can't replicate your problem (nor understand it given your description), but try something like this ? :
dfall = pd.merge(dframe, dframep, how = 'left' ,left_on = ['ID','date','instrument','close'], right_on =['ID','date','instrument','close']
This will keep the rows of dframe, and bring the info that matches from dframp