Exploring the Regina Gas Stations Dataset¶

Table of contents¶

Purpose
Loading the Dataset
1. Printing the entire dataset
Removing the empty row
Information about the database
Key Error
Counting occurrences of a specific value
Grouping Specific Column Data
Plotting the Data
Conclusion

Purpose ¶

I created this dataset to do some experiments and tell stories. I scraped every single data from the Google Maps manually. Although, this is a open source dataset, feel free to edit and update if you have any great suggestions. All the EDA tools and techniques will be applied on this data-set. Get exicited.

Loading the Dataset ¶

First you need to upload the dataset to your Google Colab. Then you need to read it to your notebook as shown below. Since this is not a big dataset interms of having many rows. I stored them in a Excel Sheet. A simple way to store big data sets is to use CSV files (comma separated files).

CSV files contains plain text and is a well know format that can be read by everyone including Pandas. To read a CSV file you might want to use pd.read_csv('data.csv'). instead of pd.read_excel which is used for excel stored datasets.

In [ ]:

import pandas as pd

# Reading the dataset
df = pd.read_excel('/content/Regina Gas Stations.xlsx')
df

Out[ ]:

	Name	Location	Phone Number
0	NaN	NaN	NaN
1	Mobil	Albert Street	306-790-1228
2	Esso	Albert Street	306-584-7185
3	Canadian Tire - Gas Plus	Albert Street	306-585-3319
4	Co-op Gas Bar	Albert Street	306-791-8890
...	...	...	...
64	Cowessess Gas	SK-33 Highway	306-359-6777
65	Petro Canada	Gravel Pit Road	306-525-6033
66	Petro Pass Regina	529 1 Ave E	306-721-6667
67	Co-op Gas Bar	Great Plains Road	306-791-5707
68	Esso	Great Plains Road	306-781-2580

69 rows × 3 columns

Printing the entire dataset ¶

If you wanted to print the entire dataset, then you can use the to_string() method. This will consume a lot of RAM but worth giving a try and know how it works.

In [ ]:

print(df.to_string())

                        Name               Location  Phone Number
0                        NaN                    NaN           NaN
1                      Mobil         Albert Street   306-790-1228
2                      Esso          Albert Street   306-584-7185
3   Canadian Tire - Gas Plus         Albert Street   306-585-3319
4              Co-op Gas Bar         Albert Street   306-791-8890
5                      Shell         Albert Street   306-586-4333
6              Petro Canada          Albert Street   306-585-1849
7                     Mobil               Broadway   306-569-0255
8             Co-op Gas Bar            Gordon Road   306-791-9388
9              Petro Canada          Albert Street   306-522-9112
10                     Esso          Albert Street   306-525-0414
11             Petro Canada        Victoria Avenue   306-352-4252
12            Race Trac Gas            Hill Avenue   306-586-5777
13              Fas Gas Plus            7th Avenue   306-569-3399
14                      Esso            7th Avenue            TBD
15       Regina Discount Gas    Saskatchewan Drive   306-924-8733
16              Checkers Gas         Pasqua Street   306-791-6362
17                      Esso         Arcola Avenue   306-525-9121
18                     Shell         Arcola Avenue   306-525-1717
19             Petro Canada          Quance Street   306-791-6934
20         Regina Messenger         Halifax Street            TBD
21  Regina Cabs Gas Station     Saskatchewan Drive   306-543-3333
22             Co-op Gas Bar         Dewdney Avenue  306-791-9308
23                     Husky            Park Street  306-522-0160
24                     Shell       Victoria Avenue   306-789-9495
25                     Shell       Victoria Avenue   306-525-1218
26                     Domo          Albert Street            TBD
27                      Esso         Albert Street   306-543-1155
28                    Shell          Albert Street   306-543-2900
29             Petro Canada            Fleet Street  306-789-7077
30                     Shell             4400 4 Ave  306-337-1252
31                    Shell         Truesdale Drive  306-789-6150
32       Regina Discount Gas        Winnipeg Street           TBD
33  Canadian Tire - Gas Plus         Albert Street   306-924-5535
34                      Esso        Winnipeg Street           TBD
35             Petro Canada          Albert Street   306-545-3533
36                      Esso    180 Albert Street N  306-949-5800
37             Petro Canada          Sherwood Drive  306-545-3533
38             Petro Canada       Bucking Ham Drive  306-501-9288
39                    Mobil   Prince of Wales Drive  306-546-6526
40             Co-op Gas Bar        Winnipeg Street  306-791-9303
41            Regina Gas Bar         Albert Street   306-791-8890
42                     Shell          McCarthy Bvld  306-949-6155
43             Co-op Gas Bar  Prince of Wales Drive  306-791-9334
44                     Husky  Prince of Wales Drive  306-789-3477
45              Fas Gas Plus          Edward Street  306-585-6657
46                  Creeland           Angus Street  306-525-8066
47                     Shell    310 McCarthy Bvld N  306-949-2550
48     Petro Pass Truck Stop            529 1 Ave E  306-924-1919
49             Co-op Gas Bar             Chuka Blvd  306-791-1922
50           Esso - Cardlock  Prince of Wales Drive           TBD
51                     Mobil          Rochdale Blvd  306-546-6626
52          Kleins Food Mart         Pasqua Street   306-791-6362
53                      Esso          Rochdale Blvd  306-949-5677
54             Co-op Gas Bar          Rochdale Blvd  306-791-9333
55                  Flying J            Ross Avenue  306-721-0070
56                      Esso            Pinkie Road  306-347-0608
57             Co-op Gas Bar    8501 Dewdney Avenue  306-791-1932
58                     Domo           Rochdale Blvd           TBD
59        Costco Gas Station           Anaquod Road  306-271-0912
60                  7-Eleven      Bucking Ham Drive           TBD
61                     TEMPO        McDonald Street           TBD
62             Petro Canada    4415 Albert Street S  306-585-1849
63                    Husky         McDonald Street  306-721-6880
64             Cowessess Gas          SK-33 Highway  306-359-6777
65             Petro Canada         Gravel Pit Road  306-525-6033
66         Petro Pass Regina            529 1 Ave E  306-721-6667
67             Co-op Gas Bar      Great Plains Road  306-791-5707
68                      Esso      Great Plains Road  306-781-2580

Removing the empty row ¶

As you can see the first row has NaN values which means its not a number value. We need to remove it.

DataFrame.drop(labels=None, *, axis=0, index=None, columns=None, level=None, inplace=False, errors='raise')

In [ ]:

df = df.drop(0)
df

Out[ ]:

	Name	Location	Phone Number
1	Mobil	Albert Street	306-790-1228
2	Esso	Albert Street	306-584-7185
3	Canadian Tire - Gas Plus	Albert Street	306-585-3319
4	Co-op Gas Bar	Albert Street	306-791-8890
5	Shell	Albert Street	306-586-4333
...	...	...	...
64	Cowessess Gas	SK-33 Highway	306-359-6777
65	Petro Canada	Gravel Pit Road	306-525-6033
66	Petro Pass Regina	529 1 Ave E	306-721-6667
67	Co-op Gas Bar	Great Plains Road	306-791-5707
68	Esso	Great Plains Road	306-781-2580

68 rows × 3 columns

Information about the Data Frame ¶

In [ ]:

df.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 68 entries, 1 to 68
Data columns (total 3 columns):
 #   Column        Non-Null Count  Dtype 
---  ------        --------------  ----- 
 0   Name          68 non-null     object
 1   Location      68 non-null     object
 2   Phone Number  68 non-null     object
dtypes: object(3)
memory usage: 1.7+ KB

What we can understand from this data is that there are 67 Gas Stations in Regina. This excludes the first row that is the table header.

Key Error ¶

This can be a nightmare sometimes. It took me several hours to find the root cause of the problem. The column names had a white space appenede at the end. Remove this and your issue will be solved.

In [ ]:

# This is to check if any white space is appended to the column names
print(df.columns.tolist())

['Name', 'Location', 'Phone Number']

Counting occurrences of a specific value ¶

data['column_name'].value_counts()[value]

where

data is the input dataframe
value is the string/integer value present in the column to be counted
column_name is the column in the dataframe

In [ ]:

print(df['Location'].value_counts())

Albert Street            14
Rochdale Blvd             4
Prince of Wales Drive     4
Winnipeg Street           3
Victoria Avenue           3
McDonald Street           2
529 1 Ave E               2
Bucking Ham Drive         2
Great Plains Road         2
7th Avenue                2
Arcola Avenue             2
Pasqua Street             2
Saskatchewan Drive        2
Quance Street             1
Gravel Pit Road           1
SK-33 Highway             1
4415 Albert Street S      1
Gordon Road               1
Anaquod Road              1
8501 Dewdney Avenue       1
Pinkie Road               1
Ross Avenue               1
Hill Avenue               1
Chuka Blvd                1
Angus Street              1
310 McCarthy Bvld N       1
Halifax Street            1
Edward Street             1
McCarthy Bvld             1
Broadway                  1
180 Albert Street N       1
Truesdale Drive           1
4400 4 Ave                1
Fleet Street              1
Park Street               1
Dewdney Avenue            1
Sherwood Drive            1
Name: Location, dtype: int64
['Name', 'Location', 'Phone Number']

Grouping Specific Column Data ¶

Grouping the column Location, this gives a count of the occurances. You call .groupby() and pass the name of the column that you want to group on, which is "Location". Then, you use ["Name"] to specify the columns on which you want to perform the actual aggregation.

In [ ]:

Location_count = df.groupby("Location")["Name"].count()
Location_count

Out[ ]:

Location
180 Albert Street N       1
310 McCarthy Bvld N       1
4400 4 Ave                1
4415 Albert Street S      1
529 1 Ave E               2
7th Avenue                2
8501 Dewdney Avenue       1
Albert Street            14
Anaquod Road              1
Angus Street              1
Arcola Avenue             2
Broadway                  1
Bucking Ham Drive         2
Chuka Blvd                1
Dewdney Avenue            1
Edward Street             1
Fleet Street              1
Gordon Road               1
Gravel Pit Road           1
Great Plains Road         2
Halifax Street            1
Hill Avenue               1
McCarthy Bvld             1
McDonald Street           2
Park Street               1
Pasqua Street             2
Pinkie Road               1
Prince of Wales Drive     4
Quance Street             1
Rochdale Blvd             4
Ross Avenue               1
SK-33 Highway             1
Saskatchewan Drive        2
Sherwood Drive            1
Truesdale Drive           1
Victoria Avenue           3
Winnipeg Street           3
Name: Name, dtype: int64

Plotting the Data ¶

For this we can use the pandas bar plot. A bar plot is a plot that presents categorical data with rectangular bars with lengths proportional to the values that they represent. A bar plot shows comparisons among discrete categories. One axis of the plot shows the specific categories being compared, and the other axis represents a measured value.

In [ ]:

Location_count.plot.bar(stacked=True, figsize = (15, 4))

Out[ ]:

<Axes: xlabel='Location'>

Conclusion ¶

Hence there I conclude there are currently 69 Gas Stations in the City of Regina. This will dynamic and hence can change frequently. I will in the future create a scraper tool that will automatically scrape the data so that I don't manually need to add new cells in the Excel Sheet. If you have any intresting EDA tools, feel free to let me know. If will definetly add it in here. My contact info is tanunprabhu95@gmail.com. Thanks for reading my article. Stay tuned for more updates. Until then see you next time.