Created
June 5, 2015 15:03
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import pandas | |
import pandasql | |
def max_temp_aggregate_by_fog(filename): | |
''' | |
This function should run a SQL query on a dataframe of | |
weather data. The SQL query should return two columns and | |
two rows - whether it was foggy or not (0 or 1) and the max | |
maxtempi for that fog value (i.e., the maximum max temperature | |
for both foggy and non-foggy days). The dataframe will be | |
titled 'weather_data'. You'll need to provide the SQL query. | |
You might also find that interpreting numbers as integers or floats may not | |
work initially. In order to get around this issue, it may be useful to cast | |
these numbers as integers. This can be done by writing cast(column as integer). | |
So for example, if we wanted to cast the maxtempi column as an integer, we would actually | |
write something like where cast(maxtempi as integer) = 76, as opposed to simply | |
where maxtempi = 76. | |
You can see the weather data that we are passing in below: | |
https://www.dropbox.com/s/7sf0yqc9ykpq3w8/weather_underground.csv | |
''' | |
weather_data = pandas.read_csv(filename) | |
q = """ | |
SELECT fog, max(cast (maxtempi as integer)) | |
FROM weather_data | |
GROUP BY fog; | |
""" | |
#Execute your SQL command against the pandas frame | |
foggy_days = pandasql.sqldf(q.lower(), locals()) | |
return foggy_days |
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