3.1 Field and Record Data

Estimated time to complete this notebook: 20 minutes

3.1.1 Separated Value Files

Let’s go back to the sunspots example from the previous module. We had downloaded some semicolon separated data and decided it was better to use a library than to write our own parser.

import requests

spots = requests.get("http://www.sidc.be/silso/INFO/snmtotcsv.php", timeout=60)
spots.text.split("\n")[0]

'1749;01;1749.042;  96.7; -1.0;   -1;1'

We want to work programmatically with Separated Value files.

These are files which have:

• Each record on a line

• Each record has multiple fields

• Fields are separated by some separator

Typical separators are the space, tab, comma, and semicolon separated values files, e.g.:

• Space separated value (e.g. field1 "field two" field3 )

• Comma separated value (e.g. field1, another field, "wow, another field")

Comma-separated-value is abbreviated CSV, and tab separated value TSV.

CSV is also used to refer to all the different sub-kinds of separated value files, i.e. some people use CSV to refer to tab, space and semicolon separated files.

CSV is not a particularly great data format, because it forces your data model to be a list of lists. Richer file formats describe “serialisations” for dictionaries and for deeper-than-two nested list structures as well.

Nevertheless, CSV files are very popular because you can always export spreadsheets as CSV files, (each cell is a field, each row is a record)

3.1.2 CSV variants

Some CSV formats define a comment character, so that rows beginning with, e.g., a #, are not treated as data, but give a human comment.

Some CSV formats define a three-deep list structure, where a double-newline separates records into blocks.

Some CSV formats assume that the first line defines the names of the fields, e.g.:

name, age
James, 39
Will, 2

3.1.3 Python CSV readers

The Python standard library has a csv module. However, it’s less powerful than the CSV capabilities in other libraries such as numpy. Here we will use pandas which is built on top of numpy.

import pandas as pd

df = pd.read_csv("http://www.sidc.be/silso/INFO/snmtotcsv.php", sep=";", header=None)
df.head()

	0	1	2	3	4	5	6
0	1749	1	1749.042	96.7	-1.0	-1	1
1	1749	2	1749.123	104.3	-1.0	-1	1
2	1749	3	1749.204	116.7	-1.0	-1	1
3	1749	4	1749.288	92.8	-1.0	-1	1
4	1749	5	1749.371	141.7	-1.0	-1	1

Pandas read_csv is a powerful CSV reader tool. A path to the data is given, this can be something on a local machine, or in this case the path is a url.

We used the sep optional argument to specify the delimeter. The optional argument header specifies if the data contains headers, and if so; the row numbers to use as column names.

The data is loaded into a DataFrame. The head method shows us the first 5 entries in the dataframe. The tail method shows us the last 5 entries.

df.tail()

	0	1	2	3	4	5	6
3290	2023	3	2023.204	122.6	17.1	947	0
3291	2023	4	2023.286	96.4	17.1	1003	0
3292	2023	5	2023.371	137.9	19.3	1054	0
3293	2023	6	2023.453	163.4	20.1	1072	0
3294	2023	7	2023.538	159.1	17.3	1039	0

df[3][0]

96.7

We can now plot the “Sunspot cycle”:

df.plot(x=2, y=3)

<Axes: xlabel='2'>

The plot command accepted an series of ‘X’ values and an series of ‘Y’ values, identified by their column number in this case, as the dataframe does not have (useful) column headers yet.

3.1.4 Naming Columns

As it happens, the columns definitions can be found on the source website (http://www.sidc.be/silso/infosnmtot)

CSV

Filename: SN_m_tot_V2.0.csv Format: Comma Separated values (adapted for import in spreadsheets) The separator is the semicolon ‘;’.

Contents:

• Column 1-2: Gregorian calendar date

  • Year

  • Month

• Column 3: Date in fraction of year.

• Column 4: Monthly mean total sunspot number.

• Column 5: Monthly mean standard deviation of the input sunspot numbers.

• Column 6: Number of observations used to compute the monthly mean total sunspot number.

• Column 7: Definitive/provisional marker. ‘1’ indicates that the value is definitive. ‘0’ indicates that the value is still provisional.

We can actually specify this to the formatter:

df_w_names = pd.read_csv(
    "http://www.sidc.be/silso/INFO/snmtotcsv.php",
    sep=";",
    header=None,
    names=["year", "month", "date", "mean", "deviation", "observations", "definitive"],
)
df_w_names.head()

	year	month	date	mean	deviation	observations	definitive
0	1749	1	1749.042	96.7	-1.0	-1	1
1	1749	2	1749.123	104.3	-1.0	-1	1
2	1749	3	1749.204	116.7	-1.0	-1	1
3	1749	4	1749.288	92.8	-1.0	-1	1
4	1749	5	1749.371	141.7	-1.0	-1	1

df_w_names.plot(x="date", y="mean")

<Axes: xlabel='date'>

Note: The plot method used for the DataFrame is just a wrapper around the matplotlib function plt.plot():

3.1.5 Typed Fields

It’s also often useful to check, and if necessary specify, the datatype of each field.

df_w_names.dtypes  # Check the data types of all columns in the DataFrame

year              int64
month             int64
date            float64
mean            float64
deviation       float64
observations      int64
definitive        int64
dtype: object

In this case the data types seem sensible, however if we wanted to convert the year into a floating point number instead, we could via:

df_w_names["year"] = df_w_names["year"].astype("float64")
df_w_names.dtypes

year            float64
month             int64
date            float64
mean            float64
deviation       float64
observations      int64
definitive        int64
dtype: object

df_w_names.head()

	year	month	date	mean	deviation	observations	definitive
0	1749.0	1	1749.042	96.7	-1.0	-1	1
1	1749.0	2	1749.123	104.3	-1.0	-1	1
2	1749.0	3	1749.204	116.7	-1.0	-1	1
3	1749.0	4	1749.288	92.8	-1.0	-1	1
4	1749.0	5	1749.371	141.7	-1.0	-1	1

3.1.6 Filtering data

Sometimes it is necessary to filter data, for example to only see the sunspots for the year 2018 you would use:

df_twenty_eighteen = df_w_names[(df_w_names["year"] == 2018)]
df_twenty_eighteen.head(20)

	year	month	date	mean	deviation	observations	definitive
3228	2018.0	1	2018.042	6.8	1.5	701	1
3229	2018.0	2	2018.122	10.7	1.1	917	1
3230	2018.0	3	2018.204	2.5	0.4	1081	1
3231	2018.0	4	2018.286	8.9	1.3	996	1
3232	2018.0	5	2018.371	13.1	1.6	1234	1
3233	2018.0	6	2018.453	15.6	1.6	1070	1
3234	2018.0	7	2018.538	1.6	0.6	1438	1
3235	2018.0	8	2018.623	8.7	1.0	1297	1
3236	2018.0	9	2018.705	3.3	0.6	1223	1
3237	2018.0	10	2018.790	4.9	1.2	1097	1
3238	2018.0	11	2018.873	4.9	0.6	771	1
3239	2018.0	12	2018.958	3.1	0.5	786	1

Even though we used

df_twenty_eighteen.head(20)

to show us the first 20 results from the dataframe, only 12 are shown as there are only 12 months in a year

If we wanted all data from 1997 to 1999 we could via:

df_nineties = df_w_names[(df_w_names["year"] >= 1997) & (df_w_names["year"] < 2000)]

df_nineties.head()

	year	month	date	mean	deviation	observations	definitive
2976	1997.0	1	1997.042	7.4	3.2	497	1
2977	1997.0	2	1997.123	11.0	2.9	545	1
2978	1997.0	3	1997.204	12.1	2.4	627	1
2979	1997.0	4	1997.288	23.0	3.3	663	1
2980	1997.0	5	1997.371	25.4	2.8	716	1

df_nineties.tail()

	year	month	date	mean	deviation	observations	definitive
3007	1999.0	8	1999.623	142.3	12.9	649	1
3008	1999.0	9	1999.707	106.3	6.5	624	1
3009	1999.0	10	1999.790	168.7	10.4	531	1
3010	1999.0	11	1999.874	188.3	12.3	406	1
3011	1999.0	12	1999.958	116.8	9.3	404	1