Hints for Exercise 6 ==================== Below are some tips for working on Exercise 6. Data format for problems 1-3 ---------------------------- The first 5 rows of the data file look like the following: .. code:: 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 (``TAVG``, ``TMAX``, and ``TMIN``) in degrees Fahrenheit. Dates of the observations are given in the format YYYYMMDD. No-data values are indicated with ``-9999``. 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 ``read_csv()`` function. For a variable number of spaces, we can simply change the ``sep`` value to be ``sep='\s+'``. Skipping the second row of a file --------------------------------- The ``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 ``skiprows=2``. 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 ``skiprows=[1]``. 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. .. ipython:: python :suppress: import pandas as pd .. ipython:: python data1 = pd.DataFrame(data=[['20170101', 'Pluto'], ['20170102', 'Panda'], ['20170103', 'Snoopy']], columns=['Time', 'Favourite_dog']) data2 = pd.DataFrame(data=[['20170101', 1], ['20170101', 2], ['20170102', 3], ['20170104', 3], ['20170104', 8]], columns=['Time', 'Value']) data1 data2 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** ``data1`` DataFrame. .. ipython:: python join1 = data1.merge(data2, on='Time') join1 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 ``join1``). 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 ``key``). 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. .. ipython:: python join2 = data1.merge(data2, on='Time', how='outer') join2 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 ``Value`` column. 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 `__.