Asked By – Martin Petri Bagger
I have a very large dataframe (around 1 million rows) with data from an experiment (60 respondents).
I would like to split the dataframe into 60 dataframes (a dataframe for each participant).
In the dataframe,
data, there is a variable called
'name', which is the unique code for each participant.
I have tried the following, but nothing happens (or execution does not stop within an hour). What I intend to do is to split the
data into smaller dataframes, and append these to a list (
import pandas as pd def splitframe(data, name='name'): n = data[name] df = pd.DataFrame(columns=data.columns) datalist =  for i in range(len(data)): if data[name][i] == n: df = df.append(data.iloc[i]) else: datalist.append(df) df = pd.DataFrame(columns=data.columns) n = data[name][i] df = df.append(data.iloc[i]) return datalist
I do not get an error message, the script just seems to run forever!
Is there a smart way to do it?
Now we will see solution for issue: Splitting dataframe into multiple dataframes
Firstly your approach is inefficient because the appending to the list on a row by basis will be slow as it has to periodically grow the list when there is insufficient space for the new entry, list comprehensions are better in this respect as the size is determined up front and allocated once.
However, I think fundamentally your approach is a little wasteful as you have a dataframe already so why create a new one for each of these users?
I would sort the dataframe by column
'name', set the index to be this and if required not drop the column.
Then generate a list of all the unique entries and then you can perform a lookup using these entries and crucially if you only querying the data, use the selection criteria to return a view on the dataframe without incurring a costly data copy.
# sort the dataframe df.sort_values(by='name', axis=1, inplace=True) # set the index to be this and don't drop df.set_index(keys=['name'], drop=False,inplace=True) # get a list of names names=df['name'].unique().tolist() # now we can perform a lookup on a 'view' of the dataframe joe = df.loc[df.name=='joe'] # now you can query all 'joes'
This question is answered By – EdChum
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