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# Introduction
In line with CrowdFlower’s survey, knowledge scientists spend 60% of their time organizing and cleansing the info.
On this article, we’ll stroll by means of constructing an information cleansing pipeline utilizing a real-life dataset from DoorDash. It accommodates almost 200,000 meals supply data, 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 Instances with DoorDash Knowledge
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 knowledge challenge, we’re tasked with creating a mannequin that predicts the overall supply length primarily based on historic supply knowledge.
Nonetheless, we received’t do the entire challenge—i.e., we received’t construct a predictive mannequin. As an alternative, we’ll use the dataset supplied within the challenge and create an information cleansing pipeline.
Our workflow consists of two main steps.
# Knowledge 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 varied sorts of knowledge evaluation. We will 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() technique. We’ll use this technique all through the article to see the adjustments in column worth counts; it’s a great indicator of lacking knowledge and total knowledge well being.
Right here is the output.
As you possibly can see, we’ve got 15 columns, however the variety of non-null values differs throughout them. This implies some columns include 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 knowledge sorts are objects; these needs to be datetime.
# Constructing Knowledge Cleansing Pipeline
On this step, we construct a structured knowledge cleansing pipeline to arrange the dataset for modeling. Every stage addresses frequent points comparable to timestamp formatting, lacking values, and irrelevant options.
// Fixing the Date and Time Columns Knowledge Varieties
Earlier than doing knowledge 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 accurately, we’ve got to transform them to the datetime format. To do this, we are able to 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 probably the most lacking knowledge. That’s why we’ll concentrate on cleansing it first.
// Knowledge 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. Nonetheless, many rows are lacking this data, which is an issue. As an illustration, it may possibly restrict how we are able to group or analyze the info. So how can we repair it?
We’ll fill these rows as an alternative of dropping them. To do this, we’ll 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
# International most-frequent class as a fallback
global_mode = df["store_primary_category"].mode().iloc[0]
# Construct store-level mapping to probably the most frequent class (quick and sturdy)
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!
It is a clear signal that we’re nonetheless coping with lacking values in some a part of the dataset. Now, with regards to knowledge cleansing, we’ve got two choices:
- Fill these lacking values
- Drop them
Provided that this dataset accommodates almost 200,000 rows, we are able to 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’ll use the dropna() technique from the pandas library. We’re setting inplace=True to use the adjustments 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 adjustments.
// What Can You Do Subsequent?
Now that we’ve got a clear dataset, right here are some things you are able to do subsequent:
- Carry out EDA to know supply patterns.
- Engineer new options like supply hours or busy dashers ratio so as to add extra which means to your evaluation.
- Analyze correlations between variables to extend your mannequin’s efficiency.
- Construct totally different regression fashions and discover the best-performing mannequin.
- Predict the supply length with the best-performing mannequin.
# Remaining Ideas
On this article, we’ve got cleaned the real-life dataset from DoorDash by addressing frequent knowledge high quality points, comparable to fixing incorrect knowledge sorts and dealing with lacking values. We constructed a easy knowledge cleansing pipeline tailor-made to this knowledge challenge and explored potential subsequent steps.
Actual-world datasets will be messier than you suppose, however there are additionally many strategies and methods to unravel these points. Thanks for studying!
Nate Rosidi is an information scientist and in product technique. He is additionally an adjunct professor educating analytics, and is the founding father of StrataScratch, a platform serving to knowledge 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, provides interview recommendation, shares knowledge science initiatives, and covers the whole lot SQL.
