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# Introduction
In line with CrowdFlower’s survey, information scientists spend 60% of their time organizing and cleansing the info.
On this article, we’ll stroll by means of constructing a knowledge cleansing pipeline utilizing a real-life dataset from DoorDash. It accommodates practically 200,000 meals supply information, every of which incorporates dozens of options comparable to supply time, whole objects, and retailer class (e.g., Mexican, Thai, or American delicacies).
# Predicting Meals Supply Occasions with DoorDash Information
DoorDash goals to estimate the time it takes to ship meals precisely, from the second a buyer locations an order to the time it arrives at their door. In this information mission, we’re tasked with creating a mannequin that predicts the whole supply length based mostly on historic supply information.
Nevertheless, we received’t do the entire mission—i.e., we received’t construct a predictive mannequin. As an alternative, we’ll use the dataset offered within the mission and create a knowledge cleansing pipeline.
Our workflow consists of two main steps.
# Information Exploration
Let’s begin by loading and viewing the primary few rows of the dataset.
// Load and Preview the Dataset
import pandas as pd
df = pd.read_csv("historical_data.csv")
df.head()
Right here is the output.
This dataset consists of datetime columns that seize the order creation time and precise supply time, which can be utilized to calculate supply length. It additionally accommodates different options comparable to retailer class, whole merchandise depend, subtotal, and minimal merchandise value, making it appropriate for numerous varieties of information evaluation. We are able to already see that there are some NaN values, which we’ll discover extra carefully within the following step.
// Discover The Columns With data()
Let’s examine all column names with the data() methodology. We’ll use this methodology all through the article to see the modifications in column worth counts; it’s indicator of lacking information and total information well being.
Right here is the output.
As you possibly can see, now we have 15 columns, however the variety of non-null values differs throughout them. This implies some columns comprise lacking values, which might have an effect on our evaluation if not dealt with correctly. One final thing: the created_at and actual_delivery_time information varieties are objects; these must be datetime.
# Constructing Information Cleansing Pipeline
On this step, we construct a structured information cleansing pipeline to organize the dataset for modeling. Every stage addresses widespread points comparable to timestamp formatting, lacking values, and irrelevant options.
// Fixing the Date and Time Columns Information Sorts
Earlier than doing information evaluation, we have to repair the columns that present the time. In any other case, the calculation that we talked about (actual_delivery_time – created_at) will go unsuitable.
What we’re fixing:
- created_at: when the order was positioned
- actual_delivery_time: when the meals arrived
These two columns are saved as objects, so to have the ability to do calculations appropriately, now we have to transform them to the datetime format. To do this, we will use datetime features in pandas. Right here is the code.
import pandas as pd
df = pd.read_csv("historical_data.csv")
# Convert timestamp strings to datetime objects
df["created_at"] = pd.to_datetime(df["created_at"], errors="coerce")
df["actual_delivery_time"] = pd.to_datetime(df["actual_delivery_time"], errors="coerce")
df.data()
Right here is the output.
As you possibly can see from the screenshot above, the created_at and actual_delivery_time are datetime objects now.
Among the many key columns, store_primary_category has the fewest non-null values (192,668), which implies it has essentially the most lacking information. That’s why we’ll give attention to cleansing it first.
// Information Imputation With mode()
One of many messiest columns within the dataset, evident from its excessive variety of lacking values, is store_primary_category. It tells us what sort of meals shops can be found, like Mexican, American, and Thai. Nevertheless, many rows are lacking this info, which is an issue. As an illustration, it might probably restrict how we will group or analyze the info. So how will we repair it?
We’ll fill these rows as a substitute of dropping them. To do this, we are going to use smarter imputation.
We write a dictionary that maps every store_id to its most frequent class, after which use that mapping to fill in lacking values. Let’s see the dataset earlier than doing that.
Right here is the code.
import numpy as np
# World most-frequent class as a fallback
global_mode = df["store_primary_category"].mode().iloc[0]
# Construct store-level mapping to essentially the most frequent class (quick and strong)
store_mode = (
df.groupby("store_id")["store_primary_category"]
.agg(lambda s: s.mode().iloc[0] if not s.mode().empty else np.nan)
)
# Fill lacking classes utilizing the store-level mode, then fall again to world mode
df["store_primary_category"] = (
df["store_primary_category"]
.fillna(df["store_id"].map(store_mode))
.fillna(global_mode)
)
df.data()
Right here is the output.
As you possibly can see from the screenshot above, the store_primary_category column now has a better non-null depend. However let’s double-check with this code.
df["store_primary_category"].isna().sum()
Right here is the output displaying the variety of NaN values. It’s zero; we removed all of them.
And let’s see the dataset after the imputation.
// Dropping Remaining NaNs
Within the earlier step, we corrected the store_primary_category, however did you discover one thing? The non-null counts throughout the columns nonetheless don’t match!
This can be a clear signal that we’re nonetheless coping with lacking values in some a part of the dataset. Now, with regards to information cleansing, now we have two choices:
- Fill these lacking values
- Drop them
On condition that this dataset accommodates practically 200,000 rows, we will afford to lose some. With smaller datasets, you’d have to be extra cautious. In that case, it’s advisable to investigate every column, set up requirements (resolve how lacking values will likely be stuffed—utilizing the imply, median, most frequent worth, or domain-specific defaults), after which fill them.
To take away the NaNs, we are going to use the dropna() methodology from the pandas library. We’re setting inplace=True to use the modifications on to the DataFrame while not having to assign it once more. Let’s see the dataset at this level.
Right here is the code.
df.dropna(inplace=True)
df.data()
Right here is the output.
As you possibly can see from the screenshot above, every column now has the identical variety of non-null values.
Let’s see the dataset after all of the modifications.
// What Can You Do Subsequent?
Now that now we have a clear dataset, right here are some things you are able to do subsequent:
- Carry out EDA to grasp supply patterns.
- Engineer new options like supply hours or busy dashers ratio so as to add extra that means to your evaluation.
- Analyze correlations between variables to extend your mannequin’s efficiency.
- Construct completely different regression fashions and discover the best-performing mannequin.
- Predict the supply length with the best-performing mannequin.
# Last Ideas
On this article, now we have cleaned the real-life dataset from DoorDash by addressing widespread information high quality points, comparable to fixing incorrect information varieties and dealing with lacking values. We constructed a easy information cleansing pipeline tailor-made to this information mission and explored potential subsequent steps.
Actual-world datasets might be messier than you assume, however there are additionally many strategies and tips to unravel these points. Thanks for studying!
Nate Rosidi is a knowledge scientist and in product technique. He is additionally an adjunct professor educating analytics, and is the founding father of StrataScratch, a platform serving to information scientists put together for his or her interviews with actual interview questions from prime firms. Nate writes on the most recent tendencies within the profession market, offers interview recommendation, shares information science initiatives, and covers every thing SQL.
