Please complete this exercise by 09:15 Wednesday, 16 October 2019.
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Cloud computing environments¶
Exercise 6 hints¶
Data format for problems 1-3¶
The first 5 rows of the data file look like the following:
STATION ELEVATION LATITUDE LONGITUDE DATE PRCP TAVG TMAX TMIN ----------------- ---------- ---------- ---------- -------- -------- -------- -------- -------- GHCND:FIE00142080 51 60.3269 24.9603 19520101 0.31 37 39 34 GHCND:FIE00142080 51 60.3269 24.9603 19520102 -9999 35 37 34 GHCND:FIE00142080 51 60.3269 24.9603 19520103 0.14 33 36 -9999
As you can see, we have rainfall data (
PRCP) in inches, and temperature data (
TMIN) in degrees Fahrenheit.
Dates of the observations are given in the format YYYYMMDD.
No-data values are indicated with
Reading in fixed-width text files¶
Rather than having separation by commas, our data file this week has a variable number of spaces between values.
Previously, we read in comma-separated values using the option
sep=',' for the Pandas
For a variable number of spaces we can either use the
delim_whitespace=True will work, but not both.
In this case, we suggest using
Skipping the second row of a file¶
skiprows=n option of the Pandas
read_csv() function is an easy way to skip the first n rows of a file when reading it.
If we wanted to skip the first two rows of our data file, we could thus use
The value for
n, however, need not be a single value, but can also be given in the form of a list.
In this way, one can skip reading the second row of a file using a list with an index value for the second row.
In other words, you can use
Joining data from one DataFrame to another¶
One quite useful functionality in Pandas is the ability to conduct a table join where data from one DataFrame is merged with another DataFrame based on a common key. Hence, making a table join requires that you have at least one common variable in both of the DataFrames that can be used to combine the data together.
Consider a following example. Let’s first create some test data to our DataFrames.
In : data1 = pd.DataFrame(data=[['20170101', 'Pluto'], ['20170102', 'Panda'], ['20170103', 'Snoopy']], columns=['Time', 'Favourite_dog']) In : data2 = pd.DataFrame(data=[['20170101', 1], ['20170101', 2], ['20170102', 3], ['20170104', 3], ['20170104', 8]], columns=['Time', 'Value']) In : data1 Out: Time Favourite_dog 0 20170101 Pluto 1 20170102 Panda 2 20170103 Snoopy In : data2 Out: Time Value 0 20170101 1 1 20170101 2 2 20170102 3 3 20170104 3 4 20170104 8
As we can see here, there different number of rows in the DataFrames. Important thing to notice is that there seems to be a common column called
Time that we can use to
join these DataFrames together. In Pandas we can conduct a table join with
merge -function. Consider following example where we join the data from
data2 DataFrame to
In : join1 = data1.merge(data2, on='Time') In : join1 Out: Time Favourite_dog Value 0 20170101 Pluto 1 1 20170101 Pluto 2 2 20170102 Panda 3
Ahaa! Now we can see that we managed to get the
Value column from
data2 in our
data1 DataFrame (here we just assigned those values to a new variable
Notice also that the
Pluto is two times in the joined DataFrame although, it was only once in the original one. Hence, Pandas automatically duplicates the values in such
columns where there are more matching values in one DataFrame compared to the other.
However, it is important to notice that there were more values in the
data2 DataFrame than in
data1. The result
join1, does not contain the values
3 and 8 that were from day
20170104 and they were omitted.
This might be okey, but in some cases it is useful to also bring all values from another DataFrame even though there would not be a matching value in the column that used for making the join (i.e. the
We can bring all the values from another DataFrame by specifyin parameter
how='outer', i.e. we will make an outer join.
Let’s consider another example with the outer join.
In : join2 = data1.merge(data2, on='Time', how='outer') In : join2 Out: Time Favourite_dog Value 0 20170101 Pluto 1.0 1 20170101 Pluto 2.0 2 20170102 Panda 3.0 3 20170103 Snoopy NaN 4 20170104 NaN 3.0 5 20170104 NaN 8.0
Cool! Nowe we have all the values included from both DataFrames and if Pandas did not find a common value in the
key column, it still kept them and inserted
NaN values into
Favourite_dog column and
Overall, knowing how to conduct a table join can be really handy in many different situations.
See more examples and documentation from official documentation of Pandas.