## How to find the common pair of data in python from given data

I have a data looks something like this

Start Time End Time Trip Duration Start Station End Station 01/01/17 15:09 01/01/17 15:14 321 A B 01/02/17 15:09 01/02/17 15:14 321 C D 12/03/17 15:09 12/03/17 15:14 321 E F 05/01/17 15:09 05/01/17 15:14 321 B D 17/02/17 15:09 17/02/17 15:14 321 A B 12/04/17 15:09 12/04/17 15:14 321 E H 13/05/17 15:09 13/05/17 15:14 321 S K 17/01/17 15:09 17/01/17 15:14 321 A B

Using the following code, I am able to find the most common start station

start_station = filtered['Start Station'].mode()[0]

I need to find the most common trip, i.e where a pair of start station and end station are same. According to the above data, the most common trip should be b/w A and B

Can anyone please tell me how to find a common trip

Use `GroupBy.size`

with `nlargest`

or `sort_values`

with `iloc`

for select last value.

Function `remove_unused_levels`

is used for remove MultiIndex values by removed values of `Series`

.

a = (df.groupby(['Start Station','End Station']) .size() .nlargest(1) .index.remove_unused_levels() .tolist() )

Or:

a = (df.groupby(['Start Station','End Station']) .size() .sort_values() .iloc[[-1]] .index.remove_unused_levels() .tolist() )

print(a) [('A', 'B')]

If want output `DataFrame`

:

df1 = (df.groupby(['Start Station','End Station']) .size() .reset_index(name='count') .nlargest(1, 'count')[['Start Station','End Station']] ) print (df1) Start Station End Station 0 A B

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You need count? Then try this:

df = pd.DataFrame({'Start':['A','B','C','D','A'],'End':['B']*5,'Trip Duration':[321]*5}) df.groupby(['Start','End'])['Trip Duration'].count().sort_values(ascending=False, na_position='first')

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I might do this

trip = (filtered["Start Station"] + " -> " + filtered["End Station"]).mode() # A -> B

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Have a look at this Groupby Split apply combine

This should give you a wide range of aggregation functions.

using groupby:

import pandas as pd counts = df.groupby(["Start_Station","End_Station"]).count() print(counts) Start_Time End_Time Trip_Duration trip_id Start_Station End_Station A B 3 3 3 3 B D 1 1 1 1 C D 1 1 1 1 E F 1 1 1 1 H 1 1 1 1 S K 1 1 1 1

using value_counts and a dummy column:

import pandas as pd df["trip_id"] = df.Start_Station + df.End_Station counts = df["trip_id"].value_counts() print(counts) AB 3 BD 1 EH 1 SK 1 EF 1 CD 1

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