jainvaibhav671 / Diminos-Case-Study

Analysis of Diminos Order Data

Diminos Case Study

import pandas as pd
# import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
%matplotlib inline

data = pd.read_csv("data/diminos_data.csv")
data.drop("order_id", axis=1, inplace=True)

from datetime import datetime

data.order_placed_at = data.order_placed_at.apply(datetime.fromisoformat)
data.order_delivered_at = data.order_delivered_at.apply(datetime.fromisoformat)
data.shape

(15000, 2)

data.head()

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	order_placed_at	order_delivered_at
0	2023-03-01 00:00:59	2023-03-01 00:18:07.443132
1	2023-03-01 00:03:59	2023-03-01 00:19:34.925241
2	2023-03-01 00:07:22	2023-03-01 00:22:28.291385
3	2023-03-01 00:07:47	2023-03-01 00:46:19.019399
4	2023-03-01 00:09:03	2023-03-01 00:25:13.619056

data["time_taken_hrs"] = data.order_delivered_at - data.order_placed_at
data.time_taken_hrs = data.time_taken_hrs.apply(lambda x: x.total_seconds() / 60 / 60)

data.head()

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	order_placed_at	order_delivered_at	time_taken_hrs
0	2023-03-01 00:00:59	2023-03-01 00:18:07.443132	0.285679
1	2023-03-01 00:03:59	2023-03-01 00:19:34.925241	0.259979
2	2023-03-01 00:07:22	2023-03-01 00:22:28.291385	0.251748
3	2023-03-01 00:07:47	2023-03-01 00:46:19.019399	0.642228
4	2023-03-01 00:09:03	2023-03-01 00:25:13.619056	0.269616

data.describe()

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	time_taken_hrs
count	15000.000000
mean	0.341656
std	1.602673
min	0.250000
25%	0.254580
50%	0.263300
75%	0.287994
max	121.663856

sns.boxplot(data)
plt.show()

sns.scatterplot(data)
plt.show()

According to the above plots we have some outliers but otherwise the delivery time is always under 20 minutes

data1 = data.drop("order_delivered_at", axis=1)
grouped = data1.groupby(pd.Grouper(key="order_placed_at", freq="2H"))
grouped = pd.DataFrame(grouped.size().reset_index(name='time_taken_hrs'))

grouped.plot(x="time_taken_hrs", y="order_placed_at", kind="scatter")
plt.show()

Almost all orders take more than 30 hours to be delivered

grouped[grouped["time_taken_hrs"] < 24].shape

(0, 2)

All orders take more than a day to be delivered