[PYTHON] Merge datasets with pandas

Latest version of pandas 0.14.0

The latest version of pandas, 0.14.0, was released on 5/30. To upgrade from the previous version 0.13.1, enter the following command.

pip install --upgrade pandas

Dataset merging

It was mentioned earlier that pandas is a high-level and powerful analysis library that provides objects called data frames. It's just (http://qiita.com/ynakayama/items/4362c439d9ea814cbe60), but you can use it to combine multiple datasets like RDBMS.

Try from the actual dataset

The read_json function of pandas can treat JSON as it is as a data frame. Here, we will use the API of the open source project House API Project. Here, information such as the number of Wifi client connections and temperature is released as open data using sensors. Let's merge this.

import pandas as pd

#Get the latest 100 Wifi connections on the 1st floor
wifi = pd.read_json('http://house-api-project.org/api/shibuhouse/wifi/clients?limit=100house-api-project.org/api/shibuhouse/wifi/1f/clients?limit=100')

#Get the latest 100 temperatures on the 1st floor
temp = pd.read_json('http://house-api-project.org/api/shibuhouse/1f/temperature?limit=100')

print( wifi.head(5) )
#=>
#                                       _id  clients                     time
#  0   {'$oid': '538c3799c3ca7c08531d645e'}       21  2014-06-02 08:36:36 UTC
#  1   {'$oid': '538c3760c3ca7c08531d6454'}       21  2014-06-02 08:35:37 UTC
#  2   {'$oid': '538c371fc3ca7c08531d6445'}       21  2014-06-02 08:34:36 UTC
#  3   {'$oid': '538c36e4c3ca7c08531d643b'}       21  2014-06-02 08:33:37 UTC
#  4   {'$oid': '538c36a7c3ca7c08531d642f'}       21  2014-06-02 08:32:36 UTC

print( temp.head(5) )
#=>
#                                       _id  temperature                     time
#  0   {'$oid': '538c37cac3ca7c08531d6465'}       34.000  2014-06-02 08:37:29 UTC
#  1   {'$oid': '538c378dc3ca7c08531d6459'}       34.000  2014-06-02 08:36:29 UTC
#  2   {'$oid': '538c3752c3ca7c08531d644d'}       34.000  2014-06-02 08:35:29 UTC
#  3   {'$oid': '538c3715c3ca7c08531d6442'}       34.000  2014-06-02 08:34:29 UTC
#  4   {'$oid': '538c36dbc3ca7c08531d6437'}       34.000  2014-06-02 08:33:30 UTC

#Merge two datasets by index
merged = pd.merge(wifi, temp, left_index=True, right_index=True)

Various options for merging

The frequently used arguments are as shown in the table below.

argument	Description
left	Merge to the left
right	Merge to the right
how	How to join(inner, outer, left, right)
on	When you want to explicitly specify the column name to use for merging
left_on/right_on	Alias used as a join key
left_index/right_index	When you want to merge by index

Dataset concatenation

The concat function concatenates datasets. It's similar to merging and confusing, but it's a good idea to read the documentation for more details.

Merge, join, and concatenate http://pandas.pydata.org/pandas-docs/stable/merging.html

In the JSON API example above, it looks like this.

concat = pd.concat([wifi, temp], ignore_index=True, axis=1)

concat.head(10)
#=>
#                                        0   1                        2  \
#  0  {'$oid': '538c4081c3ca7c08531d6616'}  20  2014-06-02 09:14:37 UTC   
#  1  {'$oid': '538c4045c3ca7c08531d660a'}  20  2014-06-02 09:13:37 UTC   
#  2  {'$oid': '538c4005c3ca7c08531d65fb'}  20  2014-06-02 09:12:37 UTC   
#  3  {'$oid': '538c3fcac3ca7c08531d65ef'}  20  2014-06-02 09:11:37 UTC   
#  4  {'$oid': '538c3f8ec3ca7c08531d65e4'}  20  2014-06-02 09:10:36 UTC   
#  5  {'$oid': '538c3f51c3ca7c08531d65d8'}  20  2014-06-02 09:09:36 UTC   
#  6  {'$oid': '538c3f17c3ca7c08531d65cf'}  20  2014-06-02 09:08:36 UTC   
#  7  {'$oid': '538c3edbc3ca7c08531d65c2'}  20  2014-06-02 09:07:37 UTC   
#  8  {'$oid': '538c3ea1c3ca7c08531d65b9'}  20  2014-06-02 09:06:36 UTC   
#  9  {'$oid': '538c3e61c3ca7c08531d65a8'}  22  2014-06-02 09:05:37 UTC   

#                                        3       4                        5  
#  0  {'$oid': '538c4077c3ca7c08531d6611'}  34.125  2014-06-02 09:14:30 UTC  
#  1  {'$oid': '538c403ac3ca7c08531d6606'}  34.125  2014-06-02 09:13:30 UTC  
#  2  {'$oid': '538c3fffc3ca7c08531d65fa'}  34.125  2014-06-02 09:12:30 UTC  
#  3  {'$oid': '538c3fc4c3ca7c08531d65ee'}  34.125  2014-06-02 09:11:31 UTC  
#  4  {'$oid': '538c3f86c3ca7c08531d65e2'}  34.125  2014-06-02 09:10:29 UTC  
#  5  {'$oid': '538c3f49c3ca7c08531d65d7'}  34.125  2014-06-02 09:09:29 UTC  
#  6  {'$oid': '538c3f0ec3ca7c08531d65cb'}  34.125  2014-06-02 09:08:29 UTC  
#  7  {'$oid': '538c3ed1c3ca7c08531d65bf'}  34.125  2014-06-02 09:07:29 UTC  
#  8  {'$oid': '538c3e96c3ca7c08531d65b3'}  34.125  2014-06-02 09:06:29 UTC  
#  9  {'$oid': '538c3e5bc3ca7c08531d65a7'}  34.125  2014-06-02 09:05:30 UTC

Various options for concatenation

The frequently used arguments are as shown in the table below.

argument	Description
objs	Specify the pandas object to be concatenated with a list etc.
axis	Row or column
join	inner or outer, the default is outer
keys	Values associated between concatenated datasets
ignore_index	Generate a new index separately from the axis to be connected

Summary

We've found that pandas makes it easy to merge and concatenate external datasets. For example, by plotting from here, you can see the possibilities of various analyzes such as drawing a scatter plot and examining the correlation.