This is a case study about the Cyclistic bike-share analysis, in this case, I will be analyzed by following the steps of the data analysis process: ask, prepare, process, analyze, share, and act.
In 2016, Cyclistic launched a successful bike-share offering. Since then, the program has grown to a fleet of 5,824 bicycles that are geotracked and locked into a network of 692 stations across Chicago. The bikes can be unlocked from one station and returned to any other station in the system at any time.
Analyze the Cyclistic data set for one year to understand how annual members and casual riders use Cyclistic bikes differently.
Lily Moreno: The director of marketing. Moreno is responsible for the development of campaigns and initiatives to promote the bike-share program. **Cyclistic marketing analytics team: ** A team of data analysts who are responsible for collecting, analyzing, and reporting data that helps guide Cyclistic marketing strategy. Cyclistic executive team: The executive team will decide whether to approve the recommended marketing program.
- A description of all data sources used
- Documentation of any cleaning or manipulation of data
- A summary of the analysis
- Supporting visualizations and key findings
- Top three to four recommendations based on the analysis
Lily Moreno(The director of marketing and my manager) has set a clear goal: Design marketing strategies aimed at converting casual riders into annual members. To do that, however, the marketing analyst team needs to better understand how annual members and casual riders differ, why casual riders would buy a membership, and how digital media could affect their marketing tactics. Moreno and her team are interested in analyzing the Cyclistic historical bike trip data to identify trends by asking three questions that will guide the future marketing program:
1. How do annual members and casual riders use Cyclistic bikes differently?
2. Why would casual riders buy Cyclistic annual memberships?
3. How can Cyclistic use digital media to influence casual riders to become members?
Moreno has assigned you the first question to answer: How do annual members and casual riders use Cyclistic bikes differently?, to answer this question I should prepare data by collecting it from a trusted source.
the raw data is located in here, and it is publicly available under this license with some privacy restriction.
ROCCC data
- Reliable: this dataset is complete, accurate and it is from a trusted source
- Original: the data collected from the first part means it is Original
- Comprehensive: the data contain information that we need to do the analysis
- Current: I choose the period from September 2021 to August 2022
- Cited: the data is here
before downloading the raw data we need to install some dependencies
install and import the libraries
import matplotlib.pyplot as plt
import seaborn as sns
import pandas as pd
import numpy as np
import gc- Pandas is python data analysis library, you can check the documentation
- Numpy is python library for scientific computing, again you can see more in the documenatation
- Matplotlib is pythonn data visualization library, see more
then, we downloaded 12 the dataset that are from September 2021 to August 2022
df_01 = pd.read_csv("/content/drive/MyDrive/dataset/divvy-tripdata_202109.csv")
df_02 = pd.read_csv("/content/drive/MyDrive/dataset/divvy-tripdata_202110.csv")
df_03 = pd.read_csv("/content/drive/MyDrive/dataset/divvy-tripdata_202111.csv")
df_04 = pd.read_csv("/content/drive/MyDrive/dataset/divvy-tripdata_202112.csv")
df_05 = pd.read_csv("/content/drive/MyDrive/dataset/divvy-tripdata_202201.csv")
df_06 = pd.read_csv("/content/drive/MyDrive/dataset/divvy-tripdata_202202.csv")
df_07 = pd.read_csv("/content/drive/MyDrive/dataset/divvy-tripdata_202203.csv")
df_08 = pd.read_csv("/content/drive/MyDrive/dataset/divvy-tripdata_202204.csv")
df_09 = pd.read_csv("/content/drive/MyDrive/dataset/divvy-tripdata_202205.csv")
df_10 = pd.read_csv("/content/drive/MyDrive/dataset/divvy-tripdata_202206.csv")
df_11 = pd.read_csv("/content/drive/MyDrive/dataset/divvy-tripdata_202207.csv")
df_12 = pd.read_csv("/content/drive/MyDrive/dataset/divvy-tripdata_202208.csv")we doing that because we need more RAM
#concate the all dataset into one
df = pd.concat([df_01, df_02, df_03, df_04, df_05, df_06, df_07, df_08, df_09, df_10,
df_11, df_12])Save df to pickle, for computation reasons.
#save df to pickle
df.to_pickle("./bike.pkl")read bike.pkl
bike = pd.read_pickle('bike.pkl')
bike.head()
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| ride_id | rideable_type | started_at | ended_at | start_station_name | start_station_id | end_station_name | end_station_id | start_lat | start_lng | end_lat | end_lng | member_casual | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 9DC7B962304CBFD8 | electric_bike | 2021-09-28 16:07:10 | 2021-09-28 16:09:54 | NaN | NaN | NaN | NaN | 41.89 | -87.68 | 41.89 | -87.67 | casual |
| 1 | F930E2C6872D6B32 | electric_bike | 2021-09-28 14:24:51 | 2021-09-28 14:40:05 | NaN | NaN | NaN | NaN | 41.94 | -87.64 | 41.98 | -87.67 | casual |
| 2 | 6EF72137900BB910 | electric_bike | 2021-09-28 00:20:16 | 2021-09-28 00:23:57 | NaN | NaN | NaN | NaN | 41.81 | -87.72 | 41.80 | -87.72 | casual |
| 3 | 78D1DE133B3DBF55 | electric_bike | 2021-09-28 14:51:17 | 2021-09-28 15:00:06 | NaN | NaN | NaN | NaN | 41.80 | -87.72 | 41.81 | -87.72 | casual |
| 4 | E03D4ACDCAEF6E00 | electric_bike | 2021-09-28 09:53:12 | 2021-09-28 10:03:44 | NaN | NaN | NaN | NaN | 41.88 | -87.74 | 41.88 | -87.71 | casual |
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#delete all
del df_01, df_02, df_03, df_04, df_05, df_06, df_07, df_08, df_09, df_10, df_11, df_12
gc.collect()0
#bike columns
bike.columns Index(['ride_id', 'rideable_type', 'started_at', 'ended_at',
'start_station_name', 'start_station_id', 'end_station_name',
'end_station_id', 'start_lat', 'start_lng', 'end_lat', 'end_lng',
'member_casual'],
dtype='object')
#dtypes
bike.dtypesride_id object
rideable_type object
started_at object
ended_at object
start_station_name object
start_station_id object
end_station_name object
end_station_id object
start_lat float64
start_lng float64
end_lat float64
end_lng float64
member_casual object
dtype: object
dataset have the same columns (ride_id, rideable_type, started_at, ended_at, start_station_name, start_station_id, end_station_id, start_lat, start_lng, end_lat, end_lng, member_casual), I didn't to rename some dataset's becaue they have same columns name
After combining all datasets into one, first clean the df dataset by the filter's out the duplicated value and looking for missing numeric values, after that, I will add more columns that beneficial to my analysis.
#df, ride_id duplicated
print(sum(bike['ride_id'].duplicated()))0
-as you can see there's no duplication in ride_id, so now let's keep forward and deal with the missing value
#df missing value for ride_id
print(sum(bike.ride_id.isnull()))
#df missing value for rideable_type
print(sum(bike.rideable_type.isnull()))
#df missing value for started_at
print(sum(bike.started_at.isnull()))
#df missing value for ended_at
print(sum(bike.ended_at.isnull()))
#df missing value for start_station_name
print(sum(bike.start_station_name.isnull()))
#df missing value for start_station_id
print(sum(bike.start_station_id.isnull()))
#df missing value for end_station_name
print(sum(bike.end_station_name.isnull()))
#df missing value for end_station_id
print(sum(bike.end_station_id.isnull()))
#df missing value for start_lat
print(sum(bike.start_lat.isnull()))
#dfmissing value for start_lng
print(sum(bike.start_lng.isnull()))
#dfmissing value for end_lat
print(sum(bike.end_lat.isnull()))
#df missing value for end_lng
print(sum(bike.end_lng.isnull()))
#df missing value for member_casual
print(sum(bike.member_casual.isnull()))0
0
0
0
884365
884363
946303
946303
0
0
5727
5727
0
The df columns missing value is start_station_name, start_station_id, end_station_name, end_station_id, end_lat, end_lng
let's filling each dataset missing value by first looking for the datatype and filling the null in terms of thier datatype
#fillna
bike["start_station_name"].ffill(axis=0, inplace=True)
bike["start_station_name"].bfill(axis=0, inplace=True)
bike["start_station_id"].ffill(axis=0, inplace=True)
bike["start_station_id"].bfill(axis=0, inplace=True)
bike["end_station_name"].ffill(axis=0, inplace=True)
bike["end_station_name"].bfill(axis=0, inplace=True)
bike["end_station_id"].ffill(axis=0, inplace=True)
bike["end_station_id"].bfill(axis=0, inplace=True)
bike["end_lat"].ffill(axis=0, inplace=True)
bike["end_lat"].bfill(axis=0, inplace=True)
bike["end_lng"].ffill(axis=0, inplace=True)
bike["end_lng"].bfill(axis=0, inplace=True)check if there is duplication and missing value
#duplucation
print(sum(bike['ride_id'].duplicated()))0
#df missing value for ride_id
print(sum(bike.ride_id.isnull()))
#df missing value for rideable_type
print(sum(bike.rideable_type.isnull()))
#df missing value for started_at
print(sum(bike.started_at.isnull()))
#df missing value for ended_at
print(sum(bike.ended_at.isnull()))
#df missing value for start_station_name
print(sum(bike.start_station_name.isnull()))
#df missing value for start_station_id
print(sum(bike.start_station_id.isnull()))
#df missing value for end_station_name
print(sum(bike.end_station_name.isnull()))
#df missing value for end_station_id
print(sum(bike.end_station_id.isnull()))
#df missing value for start_lat
print(sum(bike.start_lat.isnull()))
#dfmissing value for start_lng
print(sum(bike.start_lng.isnull()))
#dfmissing value for end_lat
print(sum(bike.end_lat.isnull()))
#df missing value for end_lng
print(sum(bike.end_lng.isnull()))
#df missing value for member_casual
print(sum(bike.member_casual.isnull()))0
0
0
0
0
0
0
0
0
0
0
0
0
5.1. converting the started_at and ended_at DateTime datatype and splitting into the day, month, year, and day of the week.
#convert the dataframe
#started_at, ended_at to datetime
bike['started_at'] = pd.to_datetime(bike['started_at'])
bike['ended_at'] = pd.to_datetime(bike['ended_at'])
bike['day'] = bike['started_at'].dt.day
bike['month'] = bike['started_at'].dt.month_name()
bike['year'] = bike['started_at'].dt.year
bike['day_of_week'] = bike['started_at'].dt.day_name()
bike['hour'] = bike['started_at'].dt.hour
bike['minute'] = bike['started_at'].dt.minute
bike['second'] = bike['started_at'].dt.second
bike.head()
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| ride_id | rideable_type | started_at | ended_at | start_station_name | start_station_id | end_station_name | end_station_id | start_lat | start_lng | end_lat | end_lng | member_casual | day | month | year | day_of_week | hour | minute | second | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 9DC7B962304CBFD8 | electric_bike | 2021-09-28 16:07:10 | 2021-09-28 16:09:54 | Clark St & Grace St | TA1307000127 | Desplaines St & Kinzie St | TA1306000003 | 41.89 | -87.68 | 41.89 | -87.67 | casual | 28 | September | 2021 | Tuesday | 16 | 7 | 10 |
| 1 | F930E2C6872D6B32 | electric_bike | 2021-09-28 14:24:51 | 2021-09-28 14:40:05 | Clark St & Grace St | TA1307000127 | Desplaines St & Kinzie St | TA1306000003 | 41.94 | -87.64 | 41.98 | -87.67 | casual | 28 | September | 2021 | Tuesday | 14 | 24 | 51 |
| 2 | 6EF72137900BB910 | electric_bike | 2021-09-28 00:20:16 | 2021-09-28 00:23:57 | Clark St & Grace St | TA1307000127 | Desplaines St & Kinzie St | TA1306000003 | 41.81 | -87.72 | 41.80 | -87.72 | casual | 28 | September | 2021 | Tuesday | 0 | 20 | 16 |
| 3 | 78D1DE133B3DBF55 | electric_bike | 2021-09-28 14:51:17 | 2021-09-28 15:00:06 | Clark St & Grace St | TA1307000127 | Desplaines St & Kinzie St | TA1306000003 | 41.80 | -87.72 | 41.81 | -87.72 | casual | 28 | September | 2021 | Tuesday | 14 | 51 | 17 |
| 4 | E03D4ACDCAEF6E00 | electric_bike | 2021-09-28 09:53:12 | 2021-09-28 10:03:44 | Clark St & Grace St | TA1307000127 | Desplaines St & Kinzie St | TA1306000003 | 41.88 | -87.74 | 41.88 | -87.71 | casual | 28 | September | 2021 | Tuesday | 9 | 53 | 12 |
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5.2. Add another column to ride_length by calculating the difference between ended_at and started_at, convert the datatype of ride_length, and than check if ride_length is numeric.
#difference betwee the ended_at and started_at
bike['ride_length'] = bike['ended_at'] - bike['started_at']
bike['ride_length'] = bike['ride_length'].dt.total_seconds()
bike['ride_lengthMean'] = bike['ride_length'].rolling(20).mean() #simple moving average
bike['ride_lengthMean'].fillna(bike['ride_lengthMean'].mean(), inplace=True)
bike['ride_lengthMean'].head(40)0 1185.211725
1 1185.211725
2 1185.211725
3 1185.211725
4 1185.211725
5 1185.211725
6 1185.211725
7 1185.211725
8 1185.211725
9 1185.211725
10 1185.211725
11 1185.211725
12 1185.211725
13 1185.211725
14 1185.211725
15 1185.211725
16 1185.211725
17 1185.211725
18 1185.211725
19 811.650000
20 863.700000
21 853.900000
22 916.850000
23 905.300000
24 907.900000
25 931.950000
26 948.150000
27 900.300000
28 956.050000
29 1125.600000
30 1184.600000
31 1198.850000
32 1212.800000
33 1237.500000
34 1250.450000
35 1239.750000
36 1254.850000
37 1261.350000
38 1161.600000
39 1189.100000
Name: ride_lengthMean, dtype: float64
bike['ride_lengthMean'].dropna(inplace=True)- convert to float64
#to_numeric, head, dtypes
bike['ride_length'] = bike['ride_length'].astype(np.float64)
print(bike.head())
print(bike.dtypes) ride_id rideable_type started_at ended_at \
0 9DC7B962304CBFD8 electric_bike 2021-09-28 16:07:10 2021-09-28 16:09:54
1 F930E2C6872D6B32 electric_bike 2021-09-28 14:24:51 2021-09-28 14:40:05
2 6EF72137900BB910 electric_bike 2021-09-28 00:20:16 2021-09-28 00:23:57
3 78D1DE133B3DBF55 electric_bike 2021-09-28 14:51:17 2021-09-28 15:00:06
4 E03D4ACDCAEF6E00 electric_bike 2021-09-28 09:53:12 2021-09-28 10:03:44
start_station_name start_station_id end_station_name \
0 Clark St & Grace St TA1307000127 Desplaines St & Kinzie St
1 Clark St & Grace St TA1307000127 Desplaines St & Kinzie St
2 Clark St & Grace St TA1307000127 Desplaines St & Kinzie St
3 Clark St & Grace St TA1307000127 Desplaines St & Kinzie St
4 Clark St & Grace St TA1307000127 Desplaines St & Kinzie St
end_station_id start_lat start_lng ... member_casual day month \
0 TA1306000003 41.89 -87.68 ... casual 28 September
1 TA1306000003 41.94 -87.64 ... casual 28 September
2 TA1306000003 41.81 -87.72 ... casual 28 September
3 TA1306000003 41.80 -87.72 ... casual 28 September
4 TA1306000003 41.88 -87.74 ... casual 28 September
year day_of_week hour minute second ride_length ride_lengthMean
0 2021 Tuesday 16 7 10 164.0 1185.211725
1 2021 Tuesday 14 24 51 914.0 1185.211725
2 2021 Tuesday 0 20 16 221.0 1185.211725
3 2021 Tuesday 14 51 17 529.0 1185.211725
4 2021 Tuesday 9 53 12 632.0 1185.211725
[5 rows x 22 columns]
ride_id object
rideable_type object
started_at datetime64[ns]
ended_at datetime64[ns]
start_station_name object
start_station_id object
end_station_name object
end_station_id object
start_lat float64
start_lng float64
end_lat float64
end_lng float64
member_casual object
day int64
month object
year int64
day_of_week object
hour int64
minute int64
second int64
ride_length float64
ride_lengthMean float64
dtype: object
#error's in ride_length
sum(bike['ride_length'] < 0)
#convert the negative to positive
bike['ride_length'] = bike['ride_length'].abs()check if ride_length has no negative number
sum(bike['ride_length'] < 0)0
7 - let's see if more then casual and member in member_casual, and I doing some calculation.
# unique the member_casual
set(bike['member_casual']){'casual', 'member'}
In member_casual there are two category object, the casual and member.
After this long step of processing data, now I can do the analysis step.
-Before jumping to the analysis, let's take the summary, structure, number of columns
#head, describe(), dtypes, columns
print(bike.head())
print(bike.describe())
print(bike.dtypes)
print(bike.columns) ride_id rideable_type started_at ended_at \
0 9DC7B962304CBFD8 electric_bike 2021-09-28 16:07:10 2021-09-28 16:09:54
1 F930E2C6872D6B32 electric_bike 2021-09-28 14:24:51 2021-09-28 14:40:05
2 6EF72137900BB910 electric_bike 2021-09-28 00:20:16 2021-09-28 00:23:57
3 78D1DE133B3DBF55 electric_bike 2021-09-28 14:51:17 2021-09-28 15:00:06
4 E03D4ACDCAEF6E00 electric_bike 2021-09-28 09:53:12 2021-09-28 10:03:44
start_station_name start_station_id end_station_name \
0 Clark St & Grace St TA1307000127 Desplaines St & Kinzie St
1 Clark St & Grace St TA1307000127 Desplaines St & Kinzie St
2 Clark St & Grace St TA1307000127 Desplaines St & Kinzie St
3 Clark St & Grace St TA1307000127 Desplaines St & Kinzie St
4 Clark St & Grace St TA1307000127 Desplaines St & Kinzie St
end_station_id start_lat start_lng ... member_casual day month \
0 TA1306000003 41.89 -87.68 ... casual 28 September
1 TA1306000003 41.94 -87.64 ... casual 28 September
2 TA1306000003 41.81 -87.72 ... casual 28 September
3 TA1306000003 41.80 -87.72 ... casual 28 September
4 TA1306000003 41.88 -87.74 ... casual 28 September
year day_of_week hour minute second ride_length ride_lengthMean
0 2021 Tuesday 16 7 10 164.0 1185.211725
1 2021 Tuesday 14 24 51 914.0 1185.211725
2 2021 Tuesday 0 20 16 221.0 1185.211725
3 2021 Tuesday 14 51 17 529.0 1185.211725
4 2021 Tuesday 9 53 12 632.0 1185.211725
[5 rows x 22 columns]
start_lat start_lng end_lat end_lng day \
count 5.883043e+06 5.883043e+06 5.883043e+06 5.883043e+06 5.883043e+06
mean 4.190104e+01 -8.764766e+01 4.190129e+01 -8.764787e+01 1.572517e+01
std 4.719795e-02 3.097188e-02 4.730615e-02 3.060118e-02 8.719473e+00
min 4.164000e+01 -8.784000e+01 4.139000e+01 -8.897000e+01 1.000000e+00
25% 4.188103e+01 -8.766201e+01 4.188103e+01 -8.766356e+01 9.000000e+00
50% 4.189993e+01 -8.764377e+01 4.190000e+01 -8.764410e+01 1.600000e+01
75% 4.193000e+01 -8.762930e+01 4.193000e+01 -8.762932e+01 2.300000e+01
max 4.563503e+01 -7.379648e+01 4.237000e+01 -8.750000e+01 3.100000e+01
year hour minute second ride_length \
count 5.883043e+06 5.883043e+06 5.883043e+06 5.883043e+06 5.883043e+06
mean 2.021661e+03 1.422414e+01 2.950921e+01 2.952116e+01 1.185279e+03
std 4.734011e-01 5.033026e+00 1.728415e+01 1.730999e+01 9.628988e+03
min 2.021000e+03 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00
25% 2.021000e+03 1.100000e+01 1.500000e+01 1.500000e+01 3.630000e+02
50% 2.022000e+03 1.500000e+01 2.900000e+01 3.000000e+01 6.430000e+02
75% 2.022000e+03 1.800000e+01 4.500000e+01 4.500000e+01 1.160000e+03
max 2.022000e+03 2.300000e+01 5.900000e+01 5.900000e+01 2.442301e+06
ride_lengthMean
count 5.883043e+06
mean 1.185212e+03
std 2.263981e+03
min -2.940000e+02
25% 7.105000e+02
50% 9.059500e+02
75% 1.203700e+03
max 1.243909e+05
ride_id object
rideable_type object
started_at datetime64[ns]
ended_at datetime64[ns]
start_station_name object
start_station_id object
end_station_name object
end_station_id object
start_lat float64
start_lng float64
end_lat float64
end_lng float64
member_casual object
day int64
month object
year int64
day_of_week object
hour int64
minute int64
second int64
ride_length float64
ride_lengthMean float64
dtype: object
Index(['ride_id', 'rideable_type', 'started_at', 'ended_at',
'start_station_name', 'start_station_id', 'end_station_name',
'end_station_id', 'start_lat', 'start_lng', 'end_lat', 'end_lng',
'member_casual', 'day', 'month', 'year', 'day_of_week', 'hour',
'minute', 'second', 'ride_length', 'ride_lengthMean'],
dtype='object')
How many percentage of each category in member_casual
convert member_casual to be string datatypes
#convert to category
bike['member_casual'] = bike['member_casual'].astype("category")
bike.dtypesride_id object
rideable_type object
started_at datetime64[ns]
ended_at datetime64[ns]
start_station_name object
start_station_id object
end_station_name object
end_station_id object
start_lat float64
start_lng float64
end_lat float64
end_lng float64
member_casual category
day int64
month object
year int64
day_of_week object
hour int64
minute int64
second int64
ride_length float64
ride_lengthMean float64
dtype: object
the count and the percentage of each category in member_casual
print(bike['member_casual'].value_counts())
print(len(bike['member_casual']))
count = bike['member_casual'].value_counts()
lenth = len(bike['member_casual'])
print((count/lenth)*100)member 3414564
casual 2468479
Name: member_casual, dtype: int64
5883043
member 58.040779
casual 41.959221
Name: member_casual, dtype: float64
-FINDING (1)
- In general, in the above the casual have great (mean, max, sum) values, and they also varied (standard deviation) than members even if the count and percentage of member are more by near to 58% of the member greater than casual, maybe this is because there are a few users casuals (42%) using the bike's for a long ride but they are not a member.
Using pivot table to sumarise the mean, sum, std, max and min
table = pd.pivot_table(bike, values=['ride_length'], index=['member_casual'],
aggfunc=[np.mean, np.sum, np.std, max, min])
print(table) mean sum std max min
ride_length ride_length ride_length ride_length ride_length
member_casual
casual 1757.901519 4.339343e+09 14716.900850 2442301.0 0.0
member 771.314240 2.633702e+09 1661.414401 93594.0 0.0
-FINDING (2)
-
here in the table above we can take an intuition about why the mean of casual is greater than member but the number of rides is way less than that of the member, that's because the ride length sum of casual(4339343000) is greater of the ride sum of member(2633702000). conversely, the length of rides of casual is less than the ride length of the member, this explains why the mean of the casual ride is greater than that of member.
chart(1)
the sum of each member and casual
plt.figure(figsize=(10,8))
ax = sns.barplot(x='member_casual',
y='ride_length', data=bike,
estimator=sum, palette='viridis')
ax.set(title="the sum of each member and casual", xlabel = "member and casual", ylabel = "sum of ride length")[Text(0, 0.5, 'sum of ride length'),
Text(0.5, 0, 'member and casual'),
Text(0.5, 1.0, 'the sum of each member and casual')]
-FINDING (3)
-
As you can see in chart(1) above, the sum of casual are more than the sum of casual.
chart(2)
- the mean of ride length of each member and casual
plt.figure(figsize=(10,8))
ax = sns.barplot(x='member_casual',
y='ride_length',
data=bike, palette="husl")
ax.set(title = "The total of ride over the member and casual", xlabel = "member and casual", ylabel = "Total ride")[Text(0, 0.5, 'Total ride'),
Text(0.5, 0, 'member and casual'),
Text(0.5, 1.0, 'The total of ride over the member and casual')]
-FINDING (4)
-
In chart(2), the mean ride length of the casual is up to 1500, while the member didn't reach 1000.
Now let's see the number of ride length in member and casual at hours
#the
table = bike.groupby(['hour', 'member_casual']).agg({'ride_length':['sum', 'mean']})
table.sort_values<bound method DataFrame.sort_values of ride_length
sum mean
hour member_casual
0 casual 89613166.0 1800.111807
member 28918606.0 792.051875
1 casual 71153342.0 2169.706105
member 18897836.0 832.503789
2 casual 47650506.0 2294.640566
member 10642252.0 825.044732
3 casual 29671576.0 2448.149835
member 6620405.0 838.875443
4 casual 20099725.0 2301.583076
member 7403995.0 813.983619
5 casual 18468543.0 1397.121038
member 22255769.0 656.009226
6 casual 38989718.0 1333.164125
member 63122792.0 684.732953
7 casual 65461521.0 1235.799230
member 124707051.0 711.895756
8 casual 94201801.0 1330.740666
member 143511171.0 697.160427
9 casual 123162135.0 1585.588019
member 104669111.0 705.279439
10 casual 197028081.0 1930.077300
member 106625224.0 753.604388
11 casual 254851470.0 1932.156710
member 130556415.0 771.742290
12 casual 294087191.0 1902.947342
member 146785007.0 754.962053
13 casual 306748264.0 1892.561522
member 145771690.0 761.782698
14 casual 321866244.0 1894.300283
member 148628877.0 785.685316
15 casual 353661714.0 1893.152512
member 177150278.0 793.285977
16 casual 361264347.0 1757.216325
member 236632986.0 805.238377
17 casual 376053750.0 1623.903158
member 290209836.0 817.310615
18 casual 343865314.0 1639.116409
member 235824140.0 805.534116
19 casual 273358126.0 1689.377208
member 167115566.0 793.917034
20 casual 204264304.0 1714.518491
member 115554476.0 781.037350
21 casual 173682345.0 1701.850424
member 88533971.0 771.914581
22 casual 148252673.0 1586.795032
member 67284398.0 766.066628
23 casual 131887127.0 1893.026080
member 46279984.0 793.498114>
-FINDING (5)
- The sum value of casual is 376053750 at five pm, (17:00), and the mean of it is 2448.15 at three am (3)
- The max vaule of member is 290209836 at five pm (17:00), and the mean value of the member is 838.9 , the same as casual at three am (3)
Lets make visual chart of what we say above. by first the mean or the average ride of both member and casual over the hour of a day, then seen the sum ride of each member and casual over hour of a day.
chart(3): The average ride of each the member and casual over the hour of day
plt.figure(figsize=(10,8))
ax = sns.barplot(x='hour',
y='ride_length', hue='member_casual',
data=bike, palette="husl")
ax.set(title = "The average ride of each the member and casual over the hour of day ",
xlabel = "hour", ylabel = "average ride")[Text(0, 0.5, 'average ride'),
Text(0.5, 0, 'hour'),
Text(0.5, 1.0, 'The average ride of each the member and casual over the hour of day ')]
-FINDING (6)
-
In the chart(3): there some pickes specaily in 3 am with highest error bars varaition, and that indicate to the high demand from casual customer
chart(4): The sum ride of each the member and casual over the hour of day
plt.figure(figsize=(10,8))
ax = sns.barplot(x='hour',
y='ride_length', hue='member_casual',
data=bike, palette="husl", estimator=sum)
ax.set(title = "The average ride of each the member and casual over the hour of day ",
xlabel = "hour", ylabel = "Total ride")[Text(0, 0.5, 'Total ride'),
Text(0.5, 0, 'hour'),
Text(0.5, 1.0, 'The average ride of each the member and casual over the hour of day ')]
-Finding(7):
-
In the chart(4): the demand keep increasing from 7 am to 8 pm for both member and casual.
-compare the casual and member at weekday
table = bike.groupby(['day_of_week', 'member_casual']).agg({'ride_length':['sum', 'mean']})
table.sort_values<bound method DataFrame.sort_values of ride_length
sum mean
day_of_week member_casual
Friday casual 579017647.0 1673.330830
member 356813857.0 755.673381
Monday casual 523173875.0 1790.563053
member 354693279.0 747.008915
Saturday casual 980528463.0 1922.363592
member 389591582.0 858.089656
Sunday casual 897296333.0 2051.211536
member 350128314.0 865.529483
Thursday casual 486413798.0 1561.576165
member 390158655.0 742.470613
Tuesday casual 432323426.0 1554.991587
member 392019038.0 730.705354
Wednesday casual 440589441.0 1502.461904
member 400297111.0 731.753756>
-Finding(8):
-
the max sum value for casual is 980528463 at Saturday, and mean at 2051.2 at Sunday. -> the max sum value for member is 400297111 in Wednesday and the mean also at Sunday by 865.52
chart(5): The average ride of each the member and casual over the days of week
plt.figure(figsize=(10,8))
ax = sns.barplot(x='day_of_week',
y='ride_length', hue='member_casual',
data=bike, palette="tab10")
ax.set(title = "The average ride of each the member and casual over the day of week",
xlabel = "day of week", ylabel = "avreage ride")[Text(0, 0.5, 'avreage ride'),
Text(0.5, 0, 'day of week'),
Text(0.5, 1.0, 'The average ride of each the member and casual over the day of week')]
-Finding(9):
-
the memeber didn't exceed the mean of 1000 through out all seven days
-
the casual are more varied secailly in sunday
chart(6): The sum ride of each the member and casual over the days of week.
plt.figure(figsize=(10,8))
ax = sns.barplot(x='day_of_week',
y='ride_length', hue='member_casual',
data=bike, palette="tab10", estimator=sum)
ax.set(title = "The total ride of each the member and casual over the day of week",
xlabel = "day of week", ylabel = "Total ride")[Text(0, 0.5, 'Total ride'),
Text(0.5, 0, 'day of week'),
Text(0.5, 1.0, 'The total ride of each the member and casual over the day of week')]
-Finding(10)
-
as you can see the chart(5) the highest value of ride are in Saturday and for casual, and wednesday for member
table = bike.groupby(['day', 'member_casual']).agg({'ride_length':['sum', 'mean']})-Finding(11):
- The sum value of causal is 195326017.0 located in day 10 , and the mean of it is 2009.04 in day five.
- Day 12 is highest sum value for member is 102655507.0 and thier greater mean is 803.23 located in day five
chart(7): The total ride of each the member and casual over the days
plt.figure(figsize=(10,8))
ax = sns.barplot(x='day',
y='ride_length', hue='member_casual',
data=bike, palette="tab10")
ax.set(title = "The average ride of each the member and casual over the days",
xlabel = "days", ylabel = "avreage ride")[Text(0, 0.5, 'avreage ride'),
Text(0.5, 0, 'days'),
Text(0.5, 1.0, 'The average ride of each the member and casual over the days')]
-chart(8): The total ride of each the member and casual over the days
plt.figure(figsize=(10,8))
ax = sns.barplot(x='day',
y='ride_length', hue='member_casual',
data=bike, palette="tab10", estimator=sum)
ax.set(title = "The total ride of each the member and casual over the days",
xlabel = "days", ylabel = "total ride")[Text(0, 0.5, 'total ride'),
Text(0.5, 0, 'days'),
Text(0.5, 1.0, 'The total ride of each the member and casual over the days')]
let's see the behaviour of member and casual in months
table = bike.groupby(['month', 'member_casual']).agg({'ride_length':['sum', 'mean']})
table.sort_values<bound method DataFrame.sort_values of ride_length
sum mean
month member_casual
April casual 224004050.0 1771.945624
member 168822497.0 689.544247
August casual 631206095.0 1758.606543
member 342910399.0 803.053805
December casual 98306691.0 1409.657446
member 117402011.0 660.296346
February casual 34319291.0 1602.507051
member 64459262.0 684.331766
January casual 33755428.0 1822.647300
member 61285423.0 718.890592
July casual 713316133.0 1756.698312
member 343592953.0 823.109225
June casual 710814978.0 1926.061650
member 336122603.0 839.985213
March casual 175930668.0 1957.351505
member 139309748.0 717.499732
May casual 519378154.0 1852.176788
member 284263503.0 802.000612
November casual 148446469.0 1388.271367
member 171746973.0 678.710341
October casual 442568951.0 1720.438152
member 280534421.0 750.124126
September casual 607296075.0 1668.900148
member 323252043.0 824.082280>
-
the sum value for casual is 713316133 located in July, and mean is 1957.35 located at March.
-
the sum value for member is 343592953.0 in July, and thier mean is 839.98 at June
-
chart(9): The averge ride of each the member and casual over the Months
plt.figure(figsize=(10,8))
ax = sns.barplot(x='month',
y='ride_length', hue='member_casual',
data=bike, palette="tab10",)
ax.set(title = "The averge ride of each the member and casual over the Months",
xlabel = "months", ylabel = "average ride")[Text(0, 0.5, 'average ride'),
Text(0.5, 0, 'months'),
Text(0.5, 1.0, 'The averge ride of each the member and casual over the Months')]
chart(10): The Total ride of each the member and casual over the Months
plt.figure(figsize=(10,8))
ax = sns.barplot(x='month',
y='ride_length', hue='member_casual',
data=bike, palette="tab10", estimator=sum)
ax.set(title = "The sum ride of each the member and casual over the Months",
xlabel = "months", ylabel = "average ride")[Text(0, 0.5, 'average ride'),
Text(0.5, 0, 'months'),
Text(0.5, 1.0, 'The sum ride of each the member and casual over the Months')]
RECOMMENDATIONS* -Based on the findings
-
first thing of my recommendations is we need to do more surveys on times that the casual is more or equal to member because it is important to know why some casual users didn't prefer to be casual
-
we need to do some marketing campings at times that there huge demand by casual
-
offer some discount for the membership especially for long rides and during summer and increasing the cost of casual for the long ride
EXPORT SUMMARY FILE FOR FURTHER ANALYSIS
bike.head()
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| ride_id | rideable_type | started_at | ended_at | start_station_name | start_station_id | end_station_name | end_station_id | start_lat | start_lng | ... | member_casual | day | month | year | day_of_week | hour | minute | second | ride_length | ride_lengthMean | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 9DC7B962304CBFD8 | electric_bike | 2021-09-28 16:07:10 | 2021-09-28 16:09:54 | Clark St & Grace St | TA1307000127 | Desplaines St & Kinzie St | TA1306000003 | 41.89 | -87.68 | ... | casual | 28 | 9 | 2021 | Tuesday | 16 | 7 | 10 | 164.0 | 1185.211725 |
| 1 | F930E2C6872D6B32 | electric_bike | 2021-09-28 14:24:51 | 2021-09-28 14:40:05 | Clark St & Grace St | TA1307000127 | Desplaines St & Kinzie St | TA1306000003 | 41.94 | -87.64 | ... | casual | 28 | 9 | 2021 | Tuesday | 14 | 24 | 51 | 914.0 | 1185.211725 |
| 2 | 6EF72137900BB910 | electric_bike | 2021-09-28 00:20:16 | 2021-09-28 00:23:57 | Clark St & Grace St | TA1307000127 | Desplaines St & Kinzie St | TA1306000003 | 41.81 | -87.72 | ... | casual | 28 | 9 | 2021 | Tuesday | 0 | 20 | 16 | 221.0 | 1185.211725 |
| 3 | 78D1DE133B3DBF55 | electric_bike | 2021-09-28 14:51:17 | 2021-09-28 15:00:06 | Clark St & Grace St | TA1307000127 | Desplaines St & Kinzie St | TA1306000003 | 41.80 | -87.72 | ... | casual | 28 | 9 | 2021 | Tuesday | 14 | 51 | 17 | 529.0 | 1185.211725 |
| 4 | E03D4ACDCAEF6E00 | electric_bike | 2021-09-28 09:53:12 | 2021-09-28 10:03:44 | Clark St & Grace St | TA1307000127 | Desplaines St & Kinzie St | TA1306000003 | 41.88 | -87.74 | ... | casual | 28 | 9 | 2021 | Tuesday | 9 | 53 | 12 | 632.0 | 1185.211725 |
5 rows × 22 columns
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week = bike.groupby(['member_casual','day_of_week'])['ride_length'].agg(['mean', 'sum'])
day = bike.groupby(['member_casual','day'])['ride_length'].agg(['mean', 'sum'])
month = bike.groupby(['member_casual','month'])['ride_length'].agg(['mean', 'sum'])
hour = bike.groupby(['member_casual','hour'])['ride_length'].agg(['mean', 'sum'])
week.to_csv('bike_week.csv', index=False)
day.to_csv('bike_day.csv', index=False)
month.to_csv('bike_month.csv', index=False)
hour.to_csv('bike_hour.csv', index=False)bike.columnsIndex(['ride_id', 'rideable_type', 'started_at', 'ended_at',
'start_station_name', 'start_station_id', 'end_station_name',
'end_station_id', 'start_lat', 'start_lng', 'end_lat', 'end_lng',
'member_casual', 'day', 'month', 'year', 'day_of_week', 'hour',
'minute', 'second', 'ride_length', 'ride_lengthMean'],
dtype='object')