Create an API from EDA or ML outcome?
$begingroup$
I have the following sample dataset (the actual dataset is over 10 million records)
Passenger Trip
0 Mark London
1 Mike Girona
2 Michael Paris
3 Max Sydney
4 Martin Amsterdam
5 Martin Barcelona
6 Martin Barcelona
7 Mark London
8 Mark Paris
9 Martin New york
10 Max Sydney
11 Max Paris
12 Max Sydney
...
...
...
And I wanted to get the destination frequently travelled by a passenger !
I was playing around in Jupyter and got the expected data with the following approach
series_px = df_px_dest.groupby('Passenger')['Trip'].apply(lambda x: x.value_counts().head(1))
df_px = series_px.to_frame()
df_px.index = df_px.index.set_names(['UName', 'DEST'])
df_px.reset_index(inplace=True)
def getNextPossibleDestByUser(pxname,df=df_px):
return df.query('UName==@pxname')['DEST'].to_string(index=False)
While the response is fine. I have few doubts now
1) What's the best way to expose the method (say in this case getNextPossibleDestByUser) as a API (pass customer name as input and get the destination as output) ?
2) Whenever the API is being called , does that mean all the 10 million records gets processed each time ? Are there anyway to optimise that ?
3) Rather than dataframe (pandas) query approach can I consider some ml models or utility functions from say scikit to solve the same problem ?
python scikit-learn pandas
asked 49 secs ago
MaddyMaddy
464
$endgroup$
add a comment |
$begingroup$
I have the following sample dataset (the actual dataset is over 10 million records)
Passenger Trip
0 Mark London
1 Mike Girona
2 Michael Paris
3 Max Sydney
4 Martin Amsterdam
5 Martin Barcelona
6 Martin Barcelona
7 Mark London
8 Mark Paris
9 Martin New york
10 Max Sydney
11 Max Paris
12 Max Sydney
...
...
...
And I wanted to get the destination frequently travelled by a passenger !
I was playing around in Jupyter and got the expected data with the following approach
series_px = df_px_dest.groupby('Passenger')['Trip'].apply(lambda x: x.value_counts().head(1))
df_px = series_px.to_frame()
df_px.index = df_px.index.set_names(['UName', 'DEST'])
df_px.reset_index(inplace=True)
def getNextPossibleDestByUser(pxname,df=df_px):
return df.query('UName==@pxname')['DEST'].to_string(index=False)
While the response is fine. I have few doubts now
1) What's the best way to expose the method (say in this case getNextPossibleDestByUser) as a API (pass customer name as input and get the destination as output) ?
2) Whenever the API is being called , does that mean all the 10 million records gets processed each time ? Are there anyway to optimise that ?
3) Rather than dataframe (pandas) query approach can I consider some ml models or utility functions from say scikit to solve the same problem ?
python scikit-learn pandas
asked 49 secs ago
MaddyMaddy
464
$endgroup$
add a comment |
$begingroup$
I have the following sample dataset (the actual dataset is over 10 million records)
Passenger Trip
0 Mark London
1 Mike Girona
2 Michael Paris
3 Max Sydney
4 Martin Amsterdam
5 Martin Barcelona
6 Martin Barcelona
7 Mark London
8 Mark Paris
9 Martin New york
10 Max Sydney
11 Max Paris
12 Max Sydney
...
...
...
And I wanted to get the destination frequently travelled by a passenger !
I was playing around in Jupyter and got the expected data with the following approach
series_px = df_px_dest.groupby('Passenger')['Trip'].apply(lambda x: x.value_counts().head(1))
df_px = series_px.to_frame()
df_px.index = df_px.index.set_names(['UName', 'DEST'])
df_px.reset_index(inplace=True)
def getNextPossibleDestByUser(pxname,df=df_px):
return df.query('UName==@pxname')['DEST'].to_string(index=False)
While the response is fine. I have few doubts now
1) What's the best way to expose the method (say in this case getNextPossibleDestByUser) as a API (pass customer name as input and get the destination as output) ?
2) Whenever the API is being called , does that mean all the 10 million records gets processed each time ? Are there anyway to optimise that ?
3) Rather than dataframe (pandas) query approach can I consider some ml models or utility functions from say scikit to solve the same problem ?
python scikit-learn pandas
asked 49 secs ago
MaddyMaddy
464
$endgroup$
I have the following sample dataset (the actual dataset is over 10 million records)
Passenger Trip
0 Mark London
1 Mike Girona
2 Michael Paris
3 Max Sydney
4 Martin Amsterdam
5 Martin Barcelona
6 Martin Barcelona
7 Mark London
8 Mark Paris
9 Martin New york
10 Max Sydney
11 Max Paris
12 Max Sydney
...
...
...
And I wanted to get the destination frequently travelled by a passenger !
I was playing around in Jupyter and got the expected data with the following approach
series_px = df_px_dest.groupby('Passenger')['Trip'].apply(lambda x: x.value_counts().head(1))
df_px = series_px.to_frame()
df_px.index = df_px.index.set_names(['UName', 'DEST'])
df_px.reset_index(inplace=True)
def getNextPossibleDestByUser(pxname,df=df_px):
return df.query('UName==@pxname')['DEST'].to_string(index=False)
While the response is fine. I have few doubts now
1) What's the best way to expose the method (say in this case getNextPossibleDestByUser) as a API (pass customer name as input and get the destination as output) ?
2) Whenever the API is being called , does that mean all the 10 million records gets processed each time ? Are there anyway to optimise that ?
3) Rather than dataframe (pandas) query approach can I consider some ml models or utility functions from say scikit to solve the same problem ?
python scikit-learn pandas
python scikit-learn pandas
asked 49 secs ago
MaddyMaddy
464
asked 49 secs ago
MaddyMaddy
464
asked 49 secs ago
MaddyMaddy
464
asked 49 secs ago
MaddyMaddy
464
asked 49 secs ago
MaddyMaddy
464
464
add a comment |
add a comment |
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