Data Wrangling with `Pandas`

The are two datasets in CSV format, both are from weather station 'USC00116760' in Petersburg, IL
Data ranges from 2015-01-01 to 2015-06-29
'Temp_116760.csv' stores temperture data, the index is day-of-year.
'Prcp_116760.csv' stores precipation data, the index is date-time.
Now how can we read the data such that they appear like the followings?

Tip: Pandas will always try to align index
Tip: try to bring up the docsting of Pandas.read_csv
Tip: use Pandas.concat to join DataFrame together



In [1]:

    
# How to read the 'Temp_116760.csv' file?



In [2]:

    
df_temp.tail()



In [3]:

    
# How to read the 'Prcp_116760.csv' file and make its index datetime dtype?



In [4]:

    
df_prcp.head()



In [5]:

    
# and I want the index to be of date-time, rather than just strings
df_prcp.index.dtype









    Out[5]:





dtype('<M8[ns]')

Try pandas.concat



In [6]:

    
# How to use concat to make a combined dataframe?

Try pandas.merge
Why merge might be the better apporach?



In [7]:

    
# How to use merge to make a combined dataframe?

Using `pivot_table` to summarize data

How many snow days and non-snow days are there for each month?
Can you generate the following result, say, with the merged data?
Dose the result make sense to you
If not, why it dosen't and how to fix it?



In [8]:

    
# Assuming your now have the combined dataframe from above, named df
# How to make the following summary table using pivot_table?

Generate the CORRECT summary table for snowy days

It can be done with just 3 method calls
TIP: lookup the pandas.DataFrame.update() method.
TIP: lookup the pandas.date_range() method.



In [ ]:

    
# How should the correct summary table look like?
# How to make it with just 3 method calls?

	TMAX	TMIN
176	278.0	172.0
177	261.0	194.0
178	211.0	150.0
179	256.0	156.0
180	233.0	156.0

	PRCP	SNOW
2015-01-01	0.0	NaN
2015-01-02	0.0	NaN
2015-01-03	33.0	NaN
2015-01-04	140.0	NaN
2015-01-05	0.0	NaN

	PRCP	SNOW	TMAX	TMIN
2015-01-01	0.0	NaN	-50.0	-150.0
2015-01-02	0.0	NaN	6.0	-94.0
2015-01-03	33.0	NaN	33.0	-33.0
2015-01-04	140.0	NaN	22.0	0.0
2015-01-05	0.0	NaN	0.0	-156.0

	PRCP	SNOW	TMAX	TMIN
2015-01-01	0.0	NaN	-50.0	-150.0
2015-01-02	0.0	NaN	6.0	-94.0
2015-01-03	33.0	NaN	33.0	-33.0
2015-01-04	140.0	NaN	22.0	0.0
2015-01-05	0.0	NaN	0.0	-156.0

SNOW	False	True
1	3.0	16.0
2	6.0	12.0
3	1.0	22.0
4	NaN	24.0
5	NaN	25.0
6	NaN	26.0

Data Wrangling with Pandas

Using pivot_table to summarize data

Generate the CORRECT summary table for snowy days

Data Wrangling with `Pandas`

Using `pivot_table` to summarize data