Exploring data using Pandas

Our first task in this week’s lesson is to learn how to read and explore data files using Pandas. Reading data files using Pandas will make life a bit easier compared to the traditional Python way of reading data files. If you’re curious about that, you can check out some of the lesson materials from previous years about reading data in the Pythonic way.

Downloading the data and preparing

  1. You can start by downloading the weather data file we will be using for this part of the lesson.

  2. If you have not already started Spyder you should do so now. You can find directions on how to open Spyder at the start of Lesson 1.

  3. Navigate in Spyder to the directory where you have stored the downloaded data file. You can do this most easily in Spyder by using the filesystem browser above the File/Variable explorer panel. Click on the file folder to select the directory where you would like to work, click Open, then click on the listed file path that is now displated beside the file folder and press Enter.

    Selecting a working directory in Spyder

    Selecting a working directory in Spyder

Reading a data file with Pandas

  1. Now we’re ready to read in our temperature data file. First, we need to import the Pandas module.

    In [1]: import pandas as pd

    That’s it, Pandas is ready to use now. Notice we imported the Pandas module with the name pd.

  2. Now we’ll read the file data into a variable called dataFrame.

    In [2]: dataFrame = pd.read_csv('Kumpula-June-2016-w-metadata.txt')

    pd.read_csv() is a general function for reading data files separated by commas, spaces, or other common separators. We use it her by simply giving the name of the file to read. If all goes as planned, you should now have a new variable defined as dataFrame that contains the data file’s contents. You can check the the contents by typing

    In [3]: print(dataFrame)
       # Data file contents: Daily temperatures (mean            min  \
0                 #                     for June 1-30           2016   
1   # Data source: https://www.ncdc.noaa.gov/cdo-w...            NaN   
2   # Data processing: Extracted temperatures from...   converted to   
3           #                  comma-separated format            NaN   
4                                                   #            NaN   
5                          # David Whipp - 02.10.2017            NaN   
6                                            YEARMODA           TEMP   
7                                            20160601           65.5   
8                                            20160602           65.8   
9                                            20160603           68.4   
10                                           20160604           57.5   
11                                           20160605           51.4   
12                                           20160606           52.2   
13                                           20160607           56.9   
14                                           20160608           54.2   
15                                           20160609           49.4   
16                                           20160610           49.5   
17                                           20160611           54.0   
18                                           20160612           55.4   
19                                           20160613           58.3   
20                                           20160614           59.7   
21                                           20160615           63.4   
22                                           20160616           57.8   
23                                           20160617           60.4   
24                                           20160618           57.3   
25                                           20160619           56.3   
26                                           20160620           59.3   
27                                           20160621           62.6   
28                                           20160622           61.7   
29                                           20160623           60.9   
30                                           20160624           61.1   
31                                           20160625           65.7   
32                                           20160626           69.6   
33                                           20160627           60.7   
34                                           20160628           65.4   
35                                           20160629           65.8   
36                                           20160630           65.7   

    max) for Kumpula  Helsinki  
0                NaN       NaN  
1                NaN       NaN  
2                NaN       NaN  
3                NaN       NaN  
4                NaN       NaN  
5                NaN       NaN  
6                MAX       MIN  
7               73.6      54.7  
8               80.8      55.0  
9               77.9      55.6  
10              70.9      47.3  
11              58.3      43.2  
12              59.7      42.8  
13              65.1      45.9  
14              60.4      47.5  
15              54.1      45.7  
16              55.9      43.0  
17              62.1      41.7  
18              64.2      46.0  
19              68.2      47.3  
20              67.8      47.8  
21              70.3      49.3  
22              67.5      55.6  
23              70.7      55.9  
24              62.8      54.0  
25              59.2      54.1  
26              69.1      52.2  
27              71.4      50.4  
28              70.2      55.4  
29              67.1      54.9  
30              68.9      56.7  
31              75.4      57.9  
32              77.7      60.3  
33              70.0      57.6  
34              73.0      55.8  
35              73.2      59.7  
36              72.7      59.2

  3. This looks OK, but there are some strange values present such as NaN. NaN stands for not a number, and might indicate some problem with reading in the contents of the file. Plus, we expected about 30 lines of data, but the index values go up to 36 when we print the contents of dataFrame. Looks like we need to investigate. We can double check the contents of the data stored in dataFrame using the Spyder editor panel. If you right-click on the data file name in the File explorer you can select Edit to view the temperature data file in the editor.

    Editing a file in Spyder

    Editing a file in Spyder

  4. Now the problem is a bit more clear.

    Metadata at the top of a file in the Spyder editor

    Metadata at the top of a file in the Spyder editor

    There is some metadata at the top of the file giving basic information about its contents and source. This isn’t data we want to process, so we need to skip over that part of the file when we load it. Fortunately, that’s easy to do in Pandas, we just need to add the skiprows parameter when we read the file, listing the number of rows to skip (8 in this case).

    In [4]: dataFrame = pd.read_csv('Kumpula-June-2016-w-metadata.txt', skiprows=8)

    Let’s now print the rows and see what changed.

    In [5]: print(dataFrame)
    YEARMODA  TEMP   MAX   MIN
0   20160601  65.5  73.6  54.7
1   20160602  65.8  80.8  55.0
2   20160603  68.4  77.9  55.6
3   20160604  57.5  70.9  47.3
4   20160605  51.4  58.3  43.2
5   20160606  52.2  59.7  42.8
6   20160607  56.9  65.1  45.9
7   20160608  54.2  60.4  47.5
8   20160609  49.4  54.1  45.7
9   20160610  49.5  55.9  43.0
10  20160611  54.0  62.1  41.7
11  20160612  55.4  64.2  46.0
12  20160613  58.3  68.2  47.3
13  20160614  59.7  67.8  47.8
14  20160615  63.4  70.3  49.3
15  20160616  57.8  67.5  55.6
16  20160617  60.4  70.7  55.9
17  20160618  57.3  62.8  54.0
18  20160619  56.3  59.2  54.1
19  20160620  59.3  69.1  52.2
20  20160621  62.6  71.4  50.4
21  20160622  61.7  70.2  55.4
22  20160623  60.9  67.1  54.9
23  20160624  61.1  68.9  56.7
24  20160625  65.7  75.4  57.9
25  20160626  69.6  77.7  60.3
26  20160627  60.7  70.0  57.6
27  20160628  65.4  73.0  55.8
28  20160629  65.8  73.2  59.7
29  20160630  65.7  72.7  59.2

    That looks more like it.

    So, what happened? Well, the file data was read into a Pandas DataFrame, which is just a two-dimensional structure used for storing data like a spreadsheet. In fact, one of the neat things in Pandas is that that DataFrames have labelled axes (rows and columns). For our example, we have the rows labeled with an index value (0 to 29), and columns labelled YEARMODA, TEMP, MAX, and MIN. This is nice because we can easily use these labels to divide up our data and make interacting with it easier as you’ll see later in the lesson.

    Now we can move on to exploring our data.

    Note

    The example above, trying to read a datafile with some header text (the metadata in this case), is very common. Reading data into Pandas is pretty easy, but it helps to have a sense of what the datafile looks like before you try to read it. The challenge can be that large datafiles might not nicely load into the Spyder editor, so it might be better to look at only the top 5-10 lines of the file rather than loading the entire thing. Fortunately, there are solutions to that problem. [1]

Exploring our dataset

So this is a big deal for us. We now have some basic Python skills and the ability to read in data from a file for processing. A normal first step when you load new data is to explore the dataset a bit to understand what is there and its format.

  1. Let’s start by looking at the different columns we have in our DataFrame. We can find this in the columns attribute that is part of the DataFrame data type, something that is known automatically for this kind of data.

    In [6]: print(dataFrame.columns)
Index(['YEARMODA', 'TEMP', 'MAX', 'MIN'], dtype='object')

    Here we see the names of the different columns in the datafile, as one might expect.

  2. We can also find information about the rows in the datafile using the index attribute.

    In [7]: print(dataFrame.index)
RangeIndex(start=0, stop=30, step=1)

    Here we see how the data is indexed, starting at 0, ending at 30 [2], and with an increment of 1 between each value. This is basically the same way in which Python lists are indexed, but it suggests that maybe there are other ways to identify the rows in data using Pandas. Again, we’ll see a bit more about this later. For now, it is also useful to point out that if you want to just know how many rows you have, you can use the len() function.

    In [8]: print(len(dataFrame.index))
30

    Attention

    Based on what we have seen so far, what would be output if you did the following using our data file?

    In [9]: dataFrame = pd.read_csv('Kumpula-June-2016-w-metadata.txt', skiprows=9)

In [10]: print(dataFrame.columns)

    Select your answer from the poll options at https://geo-python.github.io/poll/.

  3. We can also get a quick sense of the size of the dataset using the shape attribute.

    In [11]: print(dataFrame.shape)
(30, 4)

    Here we see that our dataset has 30 rows, 4 columns, just as we saw above.

  4. Now let’s consider the types of data we have in our DataFrame. First, let’s see what type of data the DataFrame is.

    In [12]: type(dataFrame)
Out[12]: pandas.core.frame.DataFrame

    No surprises here, our Pandas DataFrame is a Pandas DataFrame ;).

    What about the data? Again, finding the types of data in the columns of the DataFrame is easy.

    In [13]: print(dataFrame.dtypes)
YEARMODA      int64
TEMP        float64
MAX         float64
MIN         float64
dtype: object

    The dtypes attribute holds the data types for each column, nice. Here we see that YEARMODA is an integer value (with 64-bit precision; int64), while the other values are all decimal values with 64-bit precision (float64).

  5. We can select a single column of the data using the column name.

    In [14]: print(dataFrame['TEMP'])
0     65.5
1     65.8
2     68.4
3     57.5
4     51.4
5     52.2
6     56.9
7     54.2
8     49.4
9     49.5
10    54.0
11    55.4
12    58.3
13    59.7
14    63.4
15    57.8
16    60.4
17    57.3
18    56.3
19    59.3
20    62.6
21    61.7
22    60.9
23    61.1
24    65.7
25    69.6
26    60.7
27    65.4
28    65.8
29    65.7
Name: TEMP, dtype: float64

  6. As you can see, selecting a given column is straightforward. Furthermore, printing out its values shows not only the values, but also their data type. What about the type of the column itself?

    In [15]: type(dataFrame['TEMP'])
Out[15]: pandas.core.series.Series

    Interesting. So rather than seeing a DataFrame type or float64, a selected column from a DataFrame is called a Series in Pandas. A Pandas Series is just a 1-D list of values. In fact, you can create a Pandas Series from a Python list. If you have long lists of numbers, for instance, creating a Pandas Series will allow you to interact with these values more efficiently in terms of computing time.

    In [16]: myList = [1, 2, 3, 4, 5, 6, 7.0]

In [17]: mySeries = pd.Series(myList)

In [18]: print(mySeries)
0    1.0
1    2.0
2    3.0
3    4.0
4    5.0
5    6.0
6    7.0
dtype: float64

    As you can see, myList is converted to a Pandas Series using the ps.Series() function. Also, note that Pandas is smart about the conversion, detecting a single floating point value (7.0) and assigning all values in the Series the data type float64.

  7. Just like DataFrames, Pandas Series have a set of attributes they know about themselves and methods they can use to make calculations using the Series data. Useful methods include mean(), median(), min(), max(), and std() (the standard deviation).

    In [19]: dataFrame['TEMP'].mean()
Out[19]: 59.73

    Here, we don’t even need to store dataFrame['TEMP'] as a separate series in order to find the mean value using the mean() method.

  8. One useful function to get an overview of the basic statistics for all attributes in your DataFrame is the describe() function.

    In [20]: dataFrame.describe()
Out[20]: 
           YEARMODA       TEMP        MAX        MIN
count  3.000000e+01  30.000000  30.000000  30.000000
mean   2.016062e+07  59.730000  67.940000  51.750000
std    8.803408e+00   5.475472   6.651761   5.634484
min    2.016060e+07  49.400000  54.100000  41.700000
25%    2.016061e+07  56.450000  63.150000  47.300000
50%    2.016062e+07  60.050000  69.000000  54.050000
75%    2.016062e+07  64.900000  72.375000  55.750000
max    2.016063e+07  69.600000  80.800000  60.300000

    Here, you can quickly get the basic statistical information about all your attributes (min, max, count, std, mean, quartiles).

  9. Finally, there are occasions where you’ll need to convert data in a Series to another data type. If you’re planning to print a large number of value to the screen, for instance, it might be helpful to have those values as character strings. Data type conversions is most easily done using the astype() method.

    In [21]: print(dataFrame['TEMP'].astype(str))
0     65.5
1     65.8
2     68.4
3     57.5
4     51.4
5     52.2
6     56.9
7     54.2
8     49.4
9     49.5
10    54.0
11    55.4
12    58.3
13    59.7
14    63.4
15    57.8
16    60.4
17    57.3
18    56.3
19    59.3
20    62.6
21    61.7
22    60.9
23    61.1
24    65.7
25    69.6
26    60.7
27    65.4
28    65.8
29    65.7
Name: TEMP, dtype: object

    In this case, the object data type indicates the temperature values are stored as character strings. A more obvious case is converting to integer values.

    In [22]: print(dataFrame['TEMP'].astype(int))
0     65
1     65
2     68
3     57
4     51
5     52
6     56
7     54
8     49
9     49
10    54
11    55
12    58
13    59
14    63
15    57
16    60
17    57
18    56
19    59
20    62
21    61
22    60
23    61
24    65
25    69
26    60
27    65
28    65
29    65
Name: TEMP, dtype: int64

    Here you can clearly see the temperature values are now whole numbers.

    Caution

    Be careful with type conversions from floating point values to integers. The conversion simply drops the stuff to the right of the decimal point, so all values are rounded down to the nearest whole number. For example, 99.99 will be rounded to 99 as an integer. This can be dangerous in some cases.

    Hence, it might be good to round the values before converting them to integers. Chaining the round and type conversion functions solves this issue as the .round(0).astype(int) command first rounds the values with zero decimals and then converts those values into integers.

Footnotes

[1]

When you’re trying to think over how to read in a data file you can take advantage of common command-line tools like head. head is a simple program to read lines from the start of a data file and print them to the screen. Linux or MacOS users can use head from the command line in a Terminal window as follows

$ head Kumpula-June-2016-w-metadata.txt
# Data file contents: Daily temperatures (mean, min, max) for Kumpula, Helsinki
#                     for June 1-30, 2016
# Data source: https://www.ncdc.noaa.gov/cdo-web/search?datasetid=GHCND
# Data processing: Extracted temperatures from raw data file, converted to
#                  comma-separated format
#
# David Whipp - 02.10.2017

YEARMODA,TEMP,MAX,MIN
20160601,65.5,73.6,54.7

As you can see, head gives you the first 10 lines of the file by default. You can use the -n flag to get a larger or smaller number of lines.

$ head -n 5 Kumpula-June-2016-w-metadata.txt
# Data file contents: Daily temperatures (mean, min, max) for Kumpula, Helsinki
#                     for June 1-30, 2016
# Data source: https://www.ncdc.noaa.gov/cdo-web/search?datasetid=GHCND
# Data processing: Extracted temperatures from raw data file, converted to
#                  comma-separated format

Windows users should also be able to use head via the Anaconda Prompt available where Anaconda is listed in your installed programs. After you open the Anaconda Prompt you should be able to change into the directory containing your data file and use head.
[2]Note again here that the last value in the list of indices is not included in the range, just like when you use the range() function.