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')
Related
I'm working on a jupyter notebook, and I would like to get the average 'pcnt_change' based on 'day_of_week'. How do I do this?
A simple groupby call would do the trick here.
If df is the pandas dataframe:
df.groupby('day_of_week').mean()
would return a dataframe with average of all numeric columns in the dataframe with day_of_week as index. If you want only certain column(s) to be returned, select only the needed columns on the groupby call (for e.g.,
df[['open_price', 'high_price', 'day_of_week']].groupby('day_of_week').mean()
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.
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 been playing with aggregation in pandas dataframe. Considering the following dataframe:
df=pd.DataFrame({'a':[1,2,3,4,5,6,7,8],
'batch':['q','q','q','w','w','w','w','e'],
'c':[4,1,3,4,5,1,3,2]})
I have to do aggregation on the batch column with mean for column a and min for column c.
I used the following method to do the aggregation:
agg_dict = {'a':{'a':'mean'},'c':{'c':'min'}}
aggregated_df = df.groupby("batch").agg(agg_dict)
The problem is that I want the final data frame to have the same columns as the original data frame with the slight difference of having the aggregated values present in each of the columns.
The result of the above aggregation is a multi-index data frame, and am not sure how to convert it to an individual data frame?
I followed the link: Reverting from multiindex to single index dataframe in pandas . But, this didn't work, and the final output was still a multi-index data frame.
Great, if someone could help
you can try the following code df.groupby('batch').aggregate({'c':'min','a':mean})
I have a dataframe with a column 'date' (YYYY-MM-DD HH:MM:SS) and datetime64 type.
I want to drop/eliminate rows by selecting ranges of dates. How can I do this on python/pandas?
Thank you so much in advance
(I cannot post comments, thus I dare to put an answer) The following questions also refer to deleting or filtering a data frame based on the value of a given column:
Delete rows from a pandas DataFrame based on a conditional expression involving len(string) giving KeyError
Deleting DataFrame row in Pandas based on column value
Basically, you can pass a boolean array to the index operator [ ] of the data frame, this returns the filtered data frame. Here the pandas v1.0.1 (!) documentation of how to index data frames. Also this question is helpful.