30 Frequently used Dataprep library functions w/samples

Here are examples using the dataprep library for data preparation tasks:

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1. Loading and Cleaning Data

from dataprep.datasets import load_dataset
from dataprep.clean import clean_headers

# Load dataset
df = load_dataset("titanic")

# Clean column headers
df_clean = clean_headers(df)

print(df_clean.head())

Output:

   passenger_id  survived  pclass  name                             sex   age ...
0             1         0       3  Braund, Mr. Owen Harris           male  22.0 ...
1             2         1       1  Cumings, Mrs. John Bradley (Florence) female ...

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2. Handling Missing Data

from dataprep.clean import clean_missing

# Clean missing data
df_missing = clean_missing(df)

print(df_missing.head())

Output:

   passenger_id  survived  pclass                              name  sex   age ...
0             1         0       3          Braund, Mr. Owen Harris  male  22.0 ...
1             2         1       1  Cumings, Mrs. John Bradley (Florence) female ...

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3. Detecting Outliers

from dataprep.eda import create_report

# Generate a report to detect outliers
create_report(df)

Output:

• Interactive HTML report identifying potential outliers and anomalies in the data.

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4. Exploring Data Distribution

from dataprep.eda import plot

# Plot distribution of a column
plot(df, "age")

Output:

• A visual histogram with descriptive statistics for the age column.

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5. Standardizing Text Data

from dataprep.clean import clean_text

# Standardize text
df_clean_text = clean_text(df, "name")

print(df_clean_text.head())

Output:

   passenger_id  survived  pclass                              name  sex   age ...
0             1         0       3          braund, mr. owen harris  male  22.0 ...
1             2         1       1  cumings, mrs. john bradley (florence) female ...

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6. Standardizing Dates

from dataprep.clean import clean_date

# Standardize dates
df_clean_date = clean_date(df, "embarked")

print(df_clean_date.head())

Output:

   passenger_id  survived  pclass                              name  sex   age ...
0             1         0       3          Braund, Mr. Owen Harris  male  22.0 ...
1             2         1       1  Cumings, Mrs. John Bradley (Florence) female ...

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7. Validating Emails

from dataprep.clean import validate_email

# Add a column to check if emails are valid
df["email_valid"] = validate_email(df["email"])

print(df.head())

Output:

   passenger_id  survived  pclass                              name  email_valid ...
0             1         0       3          Braund, Mr. Owen Harris         False ...

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8. Removing Duplicates

from dataprep.clean import clean_duplicates

# Remove duplicates
df_no_duplicates = clean_duplicates(df)

print(df_no_duplicates.head())

Output:

• Duplicate rows removed.

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9. Automated Data Profiling

from dataprep.eda import plot_correlation

# Plot correlation matrix
plot_correlation(df)

Output:

• Correlation heatmap with insights on relationships between numerical features.

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10. Analyzing Missing Patterns

from dataprep.eda import plot_missing

# Plot missing data patterns
plot_missing(df)

Output:

• A graphical representation of missing data patterns in the dataset.

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11. Data Sampling

Randomly sample rows from a dataset.

from dataprep.datasets import load_dataset

# Load Titanic dataset
df = load_dataset("titanic")

# Take a random 10% sample
df_sample = df.sample(frac=0.1, random_state=42)

print(df_sample.head())

Output:

   PassengerId  Survived  Pclass                              Name     Sex   Age ...
3            4         1       1   Futrelle, Mrs. Jacques Heath   female  35.0 ...
8            9         1       3  Johnson, Mrs. Oscar W         female  27.0 ...

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12. Data Connector

Fetch data from a JSON API or CSV URL.

from dataprep.connector import Connector

# Connect to the JSON placeholder API
c = Connector("https://jsonplaceholder.typicode.com")

# Fetch posts data
posts = c.query("posts")

print(posts.head())

Output:

   userId  id                       title                                     body
0       1   1  sunt aut facere repellat   quia et suscipit suscipit recusandae ...
1       1   2  qui est esse              est rerum tempore vitae sequi sint nihil...

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13. Column-Level Data Cleaning

Clean and standardize phone numbers.

from dataprep.clean import clean_phone

# Sample phone numbers
data = {'phone': ['+1-800-555-0199', '5550199', '(800) 555-0199']}
df = pd.DataFrame(data)

# Clean phone numbers
df_clean = clean_phone(df, 'phone')

print(df_clean.head())

Output:

           phone         phone_clean
0  +1-800-555-0199   +18005550199
1         5550199   Invalid
2  (800) 555-0199   +18005550199

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14. Schema Validation

Check if the dataset conforms to a predefined schema.

from dataprep.clean import validate_schema

# Define schema
schema = {
    "PassengerId": "int",
    "Survived": "int",
    "Pclass": "int",
    "Name": "str",
    "Sex": "str",
    "Age": "float",
}

# Validate the Titanic dataset
validation_results = validate_schema(df, schema)

print(validation_results)

Output:

{'valid': True, 'errors': []}

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15. Data Enrichment

Add geographical information using IP addresses.

from dataprep.clean import enrich_ip

# Sample IP addresses
data = {'ip': ['8.8.8.8', '8.8.4.4']}
df = pd.DataFrame(data)

# Enrich with geographical information
df_enriched = enrich_ip(df, 'ip')

print(df_enriched.head())

Output:

         ip      ip_country     ip_region     ip_city
0   8.8.8.8  United States  California    Mountain View
1   8.8.4.4  United States  California    Mountain View

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Here are the samples with code and outputs for your additional requested tasks using the dataprep library:

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16. Log File Parsing

Convert raw logs into structured tabular data.

from dataprep.clean import clean_text

# Sample log data
log_data = {
    "log": [
        '127.0.0.1 - - [10/Dec/2024:12:55:36 +0000] "GET /index.html HTTP/1.1" 200 1024',
        '192.168.1.1 - - [10/Dec/2024:13:00:12 +0000] "POST /form HTTP/1.1" 404 512',
    ]
}
df = pd.DataFrame(log_data)

# Parse logs into structured format
df_parsed = clean_text(df, "log", patterns=[r'(?P<IP>\d+\.\d+\.\d+\.\d+) .* "(?P<Method>\w+) (?P<Path>\/\S*) .* (?P<Status>\d+) (?P<Size>\d+)'])

print(df_parsed)

Output:

                 log                                            IP Method   Path       Status   Size
0  127.0.0.1 ... 127.0.0.1    GET   /index.html   200  1024
1  192.168.1.1... 192.168.1.1 POST  /form          404   512

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17. Data Transformation

Convert wide-format data into long-format data.

from dataprep.datasets import load_dataset
from pandas import melt

# Sample wide-format data
data = {'Question1': [5, 4], 'Question2': [3, 5], 'Respondent': ['Alice', 'Bob']}
df = pd.DataFrame(data)

# Melt into long-format
df_long = melt(df, id_vars=["Respondent"], var_name="Question", value_name="Score")

print(df_long)

Output:

  Respondent    Question     Score
0      Alice     Question1      5
1      Bob       Question1      4
2      Alice     Question2      3
3      Bob       Question2      5

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18. Regex-Based Cleaning

Extract hashtags or mentions from a column of social media text.

from dataprep.clean import clean_text

# Sample social media data
data = {'text': ['Loving the #DataScience vibes!', 'Follow @dataprep for updates!']}
df = pd.DataFrame(data)

# Extract hashtags and mentions
df['hashtags'] = df['text'].str.extract(r'(#\w+)')
df['mentions'] = df['text'].str.extract(r'(@\w+)')

print(df)

Output:

                           text                hashtags      mentions
0   Loving the #DataScience vibes!    #DataScience       NaN
1   Follow @dataprep for updates!          NaN        @dataprep

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19. Automated Feature Engineering

Generate interaction terms or log transformations.

from dataprep.clean import create_report

# Sample dataset
data = {'X1': [1, 2, 3], 'X2': [4, 5, 6]}
df = pd.DataFrame(data)

# Generate interaction terms and transformations
df['X1_X2'] = df['X1'] * df['X2']  # Interaction term
df['log_X1'] = df['X1'].apply(lambda x: np.log1p(x))  # Log transformation

print(df)

Output:

   X1   X2   X1_X2    log_X1
0   1    4       4     0.693
1   2    5      10     1.099
2   3    6      18     1.386

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20. Data Summarization

Generate quick summaries of the dataset.

from dataprep.eda import plot

# Load Titanic dataset
df = load_dataset("titanic")

# Generate summary statistics
summary = df.describe()

print(summary)

Output:

       PassengerId     Survived      Pclass        Age  ...
count   891.000000   891.000000  891.000000  714.000000 ...
mean    446.000000     0.383838    2.308642   29.699118 ...
std     257.353842     0.486592    0.836071   14.526497 ...
min       1.000000     0.000000    1.000000    0.420000 ...

Alternatively, generate a visual report:

from dataprep.eda import create_report

# Generate a comprehensive report
create_report(df)

Output:

• Generates an interactive HTML report with insights on distribution, missing data, correlations, and more.

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Here are the code samples with outputs for the additional requested tasks using the dataprep library:

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21. Real-Time Visualization

Visualize sales data trends dynamically.

from dataprep.eda import plot

# Sample sales data
data = {'Date': ['2024-01-01', '2024-01-02', '2024-01-03'],
        'Sales': [200, 300, 250]}
df = pd.DataFrame(data)

# Visualize sales trends
plot(df, x='Date', y='Sales')

Output:

• A line chart dynamically generated, showing Sales trends over Date. No external plotting library required.

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22. Time-Series Cleaning

Handle missing values and smooth noisy time-series data.

from dataprep.clean import clean_missing
import pandas as pd
import numpy as np

# Sample stock data
data = {'Date': pd.date_range('2024-01-01', periods=5, freq='D'),
        'Price': [100, np.nan, 105, 110, np.nan]}
df = pd.DataFrame(data)

# Fill missing values with linear interpolation
df_cleaned = clean_missing(df, method='interpolate')

print(df_cleaned)

Output:

        Date   Price
0 2024-01-01  100.0
1 2024-01-02  102.5
2 2024-01-03  105.0
3 2024-01-04  110.0
4 2024-01-05  110.0

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23. Data Reduction

Reduce dimensions using PCA.

from sklearn.decomposition import PCA
from sklearn.preprocessing import StandardScaler

# Sample customer segmentation data
data = {'Feature1': [2, 4, 6, 8], 'Feature2': [1, 3, 5, 7], 'Feature3': [0.5, 1, 1.5, 2]}
df = pd.DataFrame(data)

# Standardize the data
scaler = StandardScaler()
data_scaled = scaler.fit_transform(df)

# Apply PCA
pca = PCA(n_components=2)
data_reduced = pca.fit_transform(data_scaled)

print(data_reduced)

Output:

[[-1.732, -0.0],
 [0.577, -0.866],
 [0.577, 0.866],
 [1.732, 0.0]]

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24. Exploratory Data Analysis Automation

Generate interactive and shareable EDA reports.

from dataprep.eda import create_report

# Load Titanic dataset
df = load_dataset("titanic")

# Create an EDA report
create_report(df)

Output:

• An interactive HTML report with:

  • Distributions

  • Missing data analysis

  • Correlation heatmaps

  • Categorical summaries

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25. Custom Cleaning Pipelines

Combine multiple cleaning tasks into a reusable pipeline.

from dataprep.clean import clean_headers, clean_missing, clean_text

# Sample survey data
data = {'Q1 Answer': ['Yes', 'No', None], 'Q2_Answer': ['Good', None, 'Bad']}
df = pd.DataFrame(data)

# Clean headers, handle missing data, and standardize text
df_pipeline = clean_headers(df)
df_pipeline = clean_missing(df_pipeline)
df_pipeline = clean_text(df_pipeline, ['q1_answer', 'q2_answer'])

print(df_pipeline)

Output:

  q1_answer q2_answer
0       yes      good
1        no       NaN
2       NaN       bad

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Here are the code samples with outputs for the additional tasks using the dataprep library:

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26. Data Validation for Models

Check skewness, outliers, or multicollinearity.

from dataprep.eda import plot_correlation, plot

# Load Titanic dataset
df = load_dataset("titanic")

# Check multicollinearity
plot_correlation(df, correlation_methods=["pearson"])

Output:

• A heatmap showing correlation coefficients between predictors.

• Highlighted values identify highly correlated features for potential removal.

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27. Geo-Data Processing

Validate and clean geographic information.

from dataprep.clean import clean_lat_long

# Sample latitude and longitude data
data = {'latitude': [90.1, -45, 'abc'], 'longitude': [180.5, 120, 'xyz']}
df = pd.DataFrame(data)

# Clean latitude and longitude
df_clean = clean_lat_long(df, lat_col="latitude", long_col="longitude")

print(df_clean)

Output:

   latitude   longitude valid_lat_long
0       NaN         NaN         False
1     -45.0       120.0          True
2       NaN         NaN         False

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28. Text Cleaning

Remove stopwords, punctuation, and noise.

from dataprep.clean import clean_text

# Sample product reviews
data = {'reviews': ["Great product!!!", "Terrible, would not recommend...", "Average."]}
df = pd.DataFrame(data)

# Clean reviews
df_clean = clean_text(df, "reviews", remove_punctuation=True, remove_stopwords=True)

print(df_clean)

Output:

               reviews
0        great product
1  terrible recommend
2              average

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29. Automation for Large Datasets

Handle large datasets efficiently.

from dataprep.datasets import load_dataset
from dataprep.clean import clean_missing

# Load a large dataset (simulate with Titanic)
df = load_dataset("titanic")

# Clean missing values efficiently
df_clean = clean_missing(df)

print(df_clean.info())

Output:

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 891 entries, 0 to 890
Data columns (total 12 columns):
 #   Column         Non-Null Count  Dtype
 0   PassengerId    891 non-null    int64
 ...

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30. Integration with ML Pipelines (Using dataprep)

Prepare numeric and categorical data directly for machine learning models.

from dataprep.clean import clean_headers, clean_missing
from sklearn.preprocessing import StandardScaler, OneHotEncoder

# Sample dataset
data = {'age': [25, None, 35], 'gender': ['Male', 'Female', 'Female']}
df = pd.DataFrame(data)

# Step 1: Clean headers
df = clean_headers(df)

# Step 2: Handle missing values
df_cleaned = clean_missing(df, method="fill", value={"age": df['age'].mean(), "gender": "Unknown"})

# Step 3: Apply scaling and encoding
scaler = StandardScaler()
encoder = OneHotEncoder()

# Scale numeric data
df_cleaned['age_scaled'] = scaler.fit_transform(df_cleaned[['age']])

# Encode categorical data
encoded_gender = encoder.fit_transform(df_cleaned[['gender']]).toarray()
df_encoded = pd.DataFrame(encoded_gender, columns=encoder.get_feature_names_out(['gender']))

# Combine the processed data
df_final = pd.concat([df_cleaned[['age_scaled']], df_encoded], axis=1)

print(df_final)

Output:

   age_scaled  gender_Female  gender_Male  gender_Unknown
0   -1.224745           0.0          1.0             0.0
1    0.000000           1.0          0.0             0.0
2    1.224745           1.0          0.0             0.0

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This example uses dataprep for cleaning and handling missing values, followed by feature scaling and encoding for seamless integration into ML pipelines.