The easiest way to grab data out of a web page in Python
But the bad news is that the data lives inside a web page and there’s no API that you can use to grab the raw data. So now you have to waste 30 minutes throwing together a crappy script to scrape the data. It’s not hard, but it’s a waste of time that you could spend on something useful. And somehow 30 minutes always ends up being 2 hours.
For me, this kind of thing happens all the time.
Luckily, there’s a super simple answer. The Pandas library has a built-in method to scrape tabular data from html pages called read_html():
import pandas as pd
tables = pd.read_html("https://apps.sandiego.gov/sdfiredispatch/")
print(tables[0])
view raw1.py hosted with ❤ by GitHub
It’s that simple! Pandas will find any significant html tables on the page and return each one as a new DataFrame object.
To upgrade our program from toy to real, let’s tell Pandas that row 0 of the table has column headers and ask it to convert text-based dates into time objects:
import pandas as pd
calls_df, = pd.read_html("http://apps.sandiego.gov/sdfiredispatch/", header=0, parse_dates=["Call Date"])
print(calls_df)
Which gives you this beautiful output:
Call Date Call Type Street Cross Streets Unit
0 2017-06-02 17:27:58 Medical HIGHLAND AV WIGHTMAN ST/UNIVERSITY AV E17
1 2017-06-02 17:27:58 Medical HIGHLAND AV WIGHTMAN ST/UNIVERSITY AV M34
2 2017-06-02 17:23:51 Medical EMERSON ST LOCUST ST/EVERGREEN ST E22
3 2017-06-02 17:23:51 Medical EMERSON ST LOCUST ST/EVERGREEN ST M47
4 2017-06-02 17:23:15 Medical MARAUDER WY BARON LN/FROBISHER ST E38
5 2017-06-02 17:23:15 Medical MARAUDER WY BARON LN/FROBISHER ST M41
And how that the data lives in a DataFrame, the world is yours. Wish the data was available as json records? That’s just one more line of code!
import pandas as pd
calls_df, = pd.read_html("http://apps.sandiego.gov/sdfiredispatch/", header=0, parse_dates=["Call Date"])
print(calls_df.to_json(orient="records", date_format="iso"))
view rawfake_json_api.py hosted with ❤ by GitHub
If you run that, you’ll get this beautiful json output (even with proper ISO 8601 date formatting!):
[
{
"Call Date": "2017-06-02T17:34:00.000Z",
"Call Type": "Medical",
"Street": "ROSECRANS ST",
"Cross Streets": "HANCOCK ST/ALLEY",
"Unit": "M21"
},
{
"Call Date": "2017-06-02T17:34:00.000Z",
"Call Type": "Medical",
"Street": "ROSECRANS ST",
"Cross Streets": "HANCOCK ST/ALLEY",
"Unit": "T20"
},
{
"Call Date": "2017-06-02T17:30:34.000Z",
"Call Type": "Medical",
"Street": "SPORTS ARENA BL",
"Cross Streets": "CAM DEL RIO WEST/EAST DR",
"Unit": "E20"
}
// etc...
]
You can even save the data right to a CSV or XLS file:
import pandas as pd
calls_df, = pd.read_html("http://apps.sandiego.gov/sdfiredispatch/", header=0, parse_dates=["Call Date"])
calls_df.to_csv("calls.csv", index=False)
view rawcsv.py hosted with ❤ by GitHub
Run that and double-click on calls.csv to open it up in your spreadsheet app:
And of course Pandas makes it simple to filter, sort or process the data further:
>>> calls_df.describe()
Call Date Call Type Street Cross Streets Unit
count 69 69 69 64 69
unique 29 2 29 27 60
top 2017-06-02 16:59:50 Medical CHANNEL WY LA SALLE ST/WESTERN ST E1
freq 5 66 5 5 2
first 2017-06-02 16:36:46 NaN NaN NaN NaN
last 2017-06-02 17:41:30 NaN NaN NaN NaN
>>> calls_df.groupby("Call Type").count()
Call Date Street Cross Streets Unit
Call Type
Medical 66 66 61 66
Traffic Accident (L1) 3 3 3 3
>>> calls_df["Unit"].unique()
array(['E46', 'MR33', 'T40', 'E201', 'M6', 'E34', 'M34', 'E29', 'M30',
'M43', 'M21', 'T20', 'E20', 'M20', 'E26', 'M32', 'SQ55', 'E1',
'M26', 'BLS4', 'E17', 'E22', 'M47', 'E38', 'M41', 'E5', 'M19',
'E28', 'M1', 'E42', 'M42', 'E23', 'MR9', 'PD', 'LCCNOT', 'M52',
'E45', 'M12', 'E40', 'MR40', 'M45', 'T1', 'M23', 'E14', 'M2', 'E39',
'M25', 'E8', 'M17', 'E4', 'M22', 'M37', 'E7', 'M31', 'E9', 'M39',
'SQ56', 'E10', 'M44', 'M11'], dtype=object)
view rawexamples.txt hosted with ❤ by GitHub
None of this is rocket science or anything, but I use it so often that I thought it was worth sharing. Have fun!
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