📌 Let Hypothesis Break Your Python Code Before Your Users Do

🗂 Category: PROGRAMMING

🕒 Date: 2025-10-31 | ⏱️ Read time: 19 min read

Property-based tests that find bugs you didn’t know existed.

Clean Code Tip:

Instead of creating messy intermediate DataFrames for each step of a transformation, use method chaining. For custom or complex operations that don't have a built-in method, use .pipe() to insert your own functions without breaking the chain. This creates a clean, readable, and reproducible data processing pipeline. ⛓️

Example:

import pandas as pd

# Sample data
data = {
    'region': ['North', 'South', 'North', 'South', 'East', 'West'],
    'product': ['A', 'A', 'B', 'B', 'A', 'B'],
    'sales': [100, 150, 200, 50, 300, 220],
    'cost': [80, 120, 150, 40, 210, 180]
}
df = pd.DataFrame(data)

# A custom function to apply a regional surcharge
def apply_surcharge(dataframe, region, surcharge_percent):
    df_copy = dataframe.copy()
    surcharge_rate = 1 + (surcharge_percent / 100)
    mask = df_copy['region'] == region
    df_copy.loc[mask, 'profit'] *= surcharge_rate
    return df_copy

# --- The Old, Step-by-Step Way ---
print("--- Old Way ---")
# Step 1: Filter out East and West regions
df1 = df[df['region'].isin(['North', 'South'])]
# Step 2: Calculate profit
df2 = df1.assign(profit=df1['sales'] - df1['cost'])
# Step 3: Apply the custom surcharge logic, breaking the flow
df3 = apply_surcharge(df2, region='North', surcharge_percent=5)
# Step 4: Aggregate the results
old_result = df3.groupby('region')['profit'].sum().round(2)
print(old_result)


# --- The Clean, Chained Way using .pipe() ---
print("\n--- Clean Way ---")
clean_result = (
    df
    .query("region in ['North', 'South']")
    .assign(profit=lambda d: d['sales'] - d['cost'])
    .pipe(apply_surcharge, region='North', surcharge_percent=5)
    .groupby('region')['profit']
    .sum()
    .round(2)
)
print(clean_result)

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By: @DataScienceM ✨

Clean Code Tip:

For sequential CNN architectures, defining layers individually and calling them one-by-one in the forward method creates boilerplate. Encapsulate your network trunk in an nn.Sequential container. This makes your architecture declarative, compact, and much easier to read at a glance. 🏗️

Example:

import torch
import torch.nn as nn

# --- The Verbose, Repetitive Way ---
class VerboseCNN(nn.Module):
    def __init__(self, num_classes=10):
        super().__init__()
        # Layers are defined one by one
        self.conv1 = nn.Conv2d(1, 16, kernel_size=3, padding=1)
        self.relu1 = nn.ReLU()
        self.pool1 = nn.MaxPool2d(2)
        self.conv2 = nn.Conv2d(16, 32, kernel_size=3, padding=1)
        self.relu2 = nn.ReLU()
        self.pool2 = nn.MaxPool2d(2)
        self.flatten = nn.Flatten()
        self.fc = nn.Linear(32 * 7 * 7, num_classes)

    def forward(self, x):
        # The forward pass is a long, manual chain of calls
        x = self.conv1(x)
        x = self.relu1(x)
        x = self.pool1(x)
        x = self.conv2(x)
        x = self.relu2(x)
        x = self.pool2(x)
        x = self.flatten(x)
        x = self.fc(x)
        return x

print("--- Verbose Way ---")
verbose_model = VerboseCNN()
print(verbose_model)


# --- The Clean, Declarative Way with nn.Sequential ---
class CleanCNN(nn.Module):
    def __init__(self, num_classes=10):
        super().__init__()
        # The feature extractor is a clean, sequential block
        self.features = nn.Sequential(
            nn.Conv2d(1, 16, kernel_size=3, padding=1),
            nn.ReLU(),
            nn.MaxPool2d(2),
            nn.Conv2d(16, 32, kernel_size=3, padding=1),
            nn.ReLU(),
            nn.MaxPool2d(2),
            nn.Flatten()
        )
        self.classifier = nn.Linear(32 * 7 * 7, num_classes)

    def forward(self, x):
        # The forward pass is simple and clear
        features = self.features(x)
        output = self.classifier(features)
        return output

print("\n--- Clean Way ---")
clean_model = CleanCNN()
print(clean_model)

━━━━━━━━━━━━━━━
By: @DataScienceM ✨

📌 The Machine Learning Projects Employers Want to See

🗂 Category: MACHINE LEARNING

🕒 Date: 2025-10-31 | ⏱️ Read time: 7 min read

What machine learning projects will actually get you interviews and jobs

Clean Code Tip:

When building complex architectures like ResNets, defining skip connections directly in the main forward method leads to repetitive, hard-to-read code. Encapsulate repeating patterns, like a residual block, into their own reusable nn.Module. This promotes modularity, follows the DRY principle, and makes your overall network architecture dramatically cleaner. 🧱

Example:

import torch
import torch.nn as nn

# --- The Cluttered, Repetitive Way ---
class ClutteredResNet(nn.Module):
    def __init__(self, in_channels=64, num_classes=10):
        super().__init__()
        # Defining layers for two blocks inline... gets messy fast.
        self.conv1a = nn.Conv2d(in_channels, 64, 3, padding=1)
        self.bn1a = nn.BatchNorm2d(64)
        self.conv1b = nn.Conv2d(64, 64, 3, padding=1)
        self.bn1b = nn.BatchNorm2d(64)
        
        self.conv2a = nn.Conv2d(64, 64, 3, padding=1)
        self.bn2a = nn.BatchNorm2d(64)
        self.conv2b = nn.Conv2d(64, 64, 3, padding=1)
        self.bn2b = nn.BatchNorm2d(64)
        
        self.relu = nn.ReLU(inplace=True)
        # ...imagine more blocks...

    def forward(self, x):
        # Manually implementing the first block's logic
        identity1 = x
        out = self.relu(self.bn1a(self.conv1a(x)))
        out = self.bn1b(self.conv1b(out))
        out += identity1 # The skip connection
        out = self.relu(out)
        
        # Repeating the same logic for the second block
        identity2 = out
        out = self.relu(self.bn2a(self.conv2a(out)))
        out = self.bn2b(self.conv2b(out))
        out += identity2 # Another skip connection
        out = self.relu(out)
        return out


# --- The Clean, Modular Way ---

# 1. First, create a reusable module for the repeating block
class ResidualBlock(nn.Module):
    def __init__(self, in_channels, out_channels):
        super().__init__()
        self.conv1 = nn.Conv2d(in_channels, out_channels, 3, padding=1)
        self.bn1 = nn.BatchNorm2d(out_channels)
        self.conv2 = nn.Conv2d(out_channels, out_channels, 3, padding=1)
        self.bn2 = nn.BatchNorm2d(out_channels)
        self.relu = nn.ReLU(inplace=True)

    def forward(self, x):
        identity = x
        out = self.relu(self.bn1(self.conv1(x)))
        out = self.bn2(self.conv2(out))
        out += identity # Encapsulated skip connection logic
        out = self.relu(out)
        return out

# 2. Then, compose the main model from these clean blocks
class CleanResNet(nn.Module):
    def __init__(self, in_channels=64, num_classes=10):
        super().__init__()
        # The architecture is now clear and declarative
        self.layer1 = ResidualBlock(in_channels, 64)
        self.layer2 = ResidualBlock(64, 64)
        # ... add more blocks easily ...

    def forward(self, x):
        # The forward pass is simple and readable
        x = self.layer1(x)
        x = self.layer2(x)
        return x

print("--- Clean Model Architecture ---")
model = CleanResNet()
print(model)

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By: @DataScienceM ✨

#CNN #DeepLearning #Python #Tutorial

Lesson: Building a Convolutional Neural Network (CNN) for Image Classification

This lesson will guide you through building a CNN from scratch using TensorFlow and Keras to classify images from the CIFAR-10 dataset.

---

Part 1: Setup and Data Loading

First, we import the necessary libraries and load the CIFAR-10 dataset. This dataset contains 60,000 32x32 color images in 10 classes.

import tensorflow as tf
from tensorflow.keras import datasets, layers, models
import matplotlib.pyplot as plt
import numpy as np

# Load the CIFAR-10 dataset
(x_train, y_train), (x_test, y_test) = datasets.cifar10.load_data()

# Check the shape of the data
print("Training data shape:", x_train.shape)
print("Test data shape:", x_test.shape)

#TensorFlow #Keras #DataLoading

---

Part 2: Data Exploration and Preprocessing

We need to prepare the data before feeding it to the network. This involves:
• Normalization: Scaling pixel values from the 0-255 range to the 0-1 range.
• One-Hot Encoding: Converting class vectors (integers) to a binary matrix.

Let's also visualize some images to understand our data.

# Define class names for CIFAR-10
class_names = ['airplane', 'automobile', 'bird', 'cat', 'deer', 'dog', 'frog', 'horse', 'ship', 'truck']

# Visualize a few images
plt.figure(figsize=(10,10))
for i in range(25):
    plt.subplot(5,5,i+1)
    plt.xticks([])
    plt.yticks([])
    plt.grid(False)
    plt.imshow(x_train[i])
    plt.xlabel(class_names[y_train[i][0]])
plt.show()

# Normalize pixel values to be between 0 and 1
x_train = x_train.astype('float32') / 255.0
x_test = x_test.astype('float32') / 255.0

# One-hot encode the labels
y_train = tf.keras.utils.to_categorical(y_train, num_classes=10)
y_test = tf.keras.utils.to_categorical(y_test, num_classes=10)

#DataPreprocessing #Normalization #Visualization

---

Part 3: Building the CNN Model

Now, we'll construct our CNN model. A common architecture consists of a stack of Conv2D and MaxPooling2D layers, followed by Dense layers for classification.

• Conv2D: Extracts features (like edges, corners) from the input image.
• MaxPooling2D: Reduces the spatial dimensions (downsampling), which helps in making the feature detection more robust.
• Flatten: Converts the 2D feature maps into a 1D vector.
• Dense: A standard fully-connected neural network layer.

model = models.Sequential()

# Convolutional Base
model.add(layers.Conv2D(32, (3, 3), activation='relu', input_shape=(32, 32, 3)))
model.add(layers.MaxPooling2D((2, 2)))
model.add(layers.Conv2D(64, (3, 3), activation='relu'))
model.add(layers.MaxPooling2D((2, 2)))
model.add(layers.Conv2D(64, (3, 3), activation='relu'))

# Flatten and Dense Layers
model.add(layers.Flatten())
model.add(layers.Dense(64, activation='relu'))
model.add(layers.Dense(10, activation='softmax')) # 10 output classes

# Print the model summary
model.summary()

#ModelBuilding #CNN #KerasLayers

---

Part 4: Compiling the Model

Before training, we need to configure the learning process. This is done via the compile() method, which requires:
• Optimizer: An algorithm to update the model's weights (e.g., 'adam').
• Loss Function: A function to measure how inaccurate the model is during training (e.g., 'categorical_crossentropy' for multi-class classification).
• Metrics: Used to monitor the training and testing steps (e.g., 'accuracy').

model.compile(optimizer='adam',
              loss='categorical_crossentropy',
              metrics=['accuracy'])

#ModelCompilation #Optimizer #LossFunction

---

Part 5: Training the Model

We train the model using the fit() method, providing our training data, batch size, number of epochs, and validation data to monitor performance on unseen data.

history = model.fit(x_train, y_train, 
                    epochs=15, 
                    batch_size=64,
                    validation_data=(x_test, y_test))

#Training #MachineLearning #ModelFit

---

Part 6: Evaluating and Discussing Results

After training, we evaluate the model's performance on the test set. We also plot the training history to visualize accuracy and loss curves. This helps us understand if the model is overfitting or underfitting.

# Evaluate the model on the test data
test_loss, test_acc = model.evaluate(x_test, y_test, verbose=2)
print(f'\nTest accuracy: {test_acc:.4f}')

# Plot training & validation accuracy values
plt.figure(figsize=(12, 4))
plt.subplot(1, 2, 1)
plt.plot(history.history['accuracy'])
plt.plot(history.history['val_accuracy'])
plt.noscript('Model accuracy')
plt.ylabel('Accuracy')
plt.xlabel('Epoch')
plt.legend(['Train', 'Test'], loc='upper left')

# Plot training & validation loss values
plt.subplot(1, 2, 2)
plt.plot(history.history['loss'])
plt.plot(history.history['val_loss'])
plt.noscript('Model loss')
plt.ylabel('Loss')
plt.xlabel('Epoch')
plt.legend(['Train', 'Test'], loc='upper left')

plt.show()

Discussion:
The plots show how accuracy and loss change over epochs. Ideally, both training and validation accuracy should increase, while losses decrease. If the validation accuracy plateaus or decreases while training accuracy continues to rise, it's a sign of overfitting. Our simple model achieves a decent accuracy. To improve it, one could use techniques like Data Augmentation, Dropout layers, or a deeper architecture.

#Evaluation #Results #Accuracy #Overfitting

---

Part 7: Making Predictions on a Single Image

This is how you handle a single image file for prediction. The model expects a batch of images as input, so we must add an extra dimension to our single image before passing it to model.predict().

# Select a single image from the test set
img_index = 15
test_image = x_test[img_index]
true_label_index = np.argmax(y_test[img_index])

# Display the image
plt.imshow(test_image)
plt.noscript(f"Actual Label: {class_names[true_label_index]}")
plt.show()

# The model expects a batch of images, so we add a dimension
image_for_prediction = np.expand_dims(test_image, axis=0)
print("Image shape before prediction:", test_image.shape)
print("Image shape after adding batch dimension:", image_for_prediction.shape)

# Make a prediction
predictions = model.predict(image_for_prediction)
predicted_label_index = np.argmax(predictions[0])

# Print the result
print(f"\nPrediction Probabilities: {predictions[0]}")
print(f"Predicted Label: {class_names[predicted_label_index]}")
print(f"Actual Label: {class_names[true_label_index]}")

#Prediction #ImageProcessing #Inference

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By: @DataScienceM ✨

#YOLOv8 #ComputerVision #ObjectDetection #IndustrialAI #Python

Applying YOLOv8 for Industrial Automation: Counting Plastic Bottles

This lesson will guide you through a complete computer vision project using YOLOv8. The goal is to detect and count plastic bottles in an image from an industrial setting, such as a conveyor belt or a storage area.

---

Step 1: Setup and Installation

First, we need to install the necessary libraries. The ultralytics library provides the YOLOv8 model, and opencv-python is essential for image processing tasks.

#Setup #Installation

# Open your terminal or command prompt and run this command:
pip install ultralytics opencv-python

---

Step 2: Loading the Model and the Target Image

We will load a pre-trained YOLOv8 model. These models are trained on the large COCO dataset, which already knows how to identify common objects like 'bottle'. Then, we'll load our industrial image. Ensure you have an image named factory_bottles.jpg in your project folder.

#ModelLoading #DataHandling

import cv2
from ultralytics import YOLO

# Load a pre-trained YOLOv8 model (yolov8n.pt is the smallest and fastest)
model = YOLO('yolov8n.pt')

# Load the image from the industrial setting
image_path = 'factory_bottles.jpg' # Make sure this image is in your directory
img = cv2.imread(image_path)

# A quick check to ensure the image was loaded correctly
if img is None:
    print(f"Error: Could not load image at {image_path}")
else:
    print("YOLOv8 model and image loaded successfully.")

---

Step 3: Performing Detection on the Image

With the model and image loaded, we can now run the detection. The ultralytics library makes this process incredibly simple. The model will analyze the image and identify all the objects it recognizes.

#Inference #ObjectDetection

# Run the model on the image to get detection results
results = model(img)

print("Detection complete. Processing results...")

---

Step 4: Filtering and Counting the Bottles

The model detects many types of objects. Our task is to go through the results, filter for only the 'bottle' class, and count how many there are. We'll also store the locations (bounding boxes) of each detected bottle for visualization.

#DataProcessing #Filtering

# Initialize a counter for the bottles
bottle_count = 0
bottle_boxes = []

# The model's results is a list, so we loop through it
for result in results:
    # Each result has a 'boxes' attribute with the detections
    boxes = result.boxes
    for box in boxes:
        # Get the class ID of the detected object
        class_id = int(box.cls)
        # Check if the class name is 'bottle'
        if model.names[class_id] == 'bottle':
            bottle_count += 1
            # Store the bounding box coordinates (x1, y1, x2, y2)
            bottle_boxes.append(box.xyxy[0])

print(f"Total plastic bottles detected: {bottle_count}")

---

Step 5: Visualizing the Results

A number is good, but seeing what the model detected is better. We will draw the bounding boxes and the final count directly onto the image to create a clear visual output.

#Visualization #OpenCV

# Create a copy of the original image to draw on
output_img = img.copy()

# Draw a bounding box for each detected bottle
for box in bottle_boxes:
    x1, y1, x2, y2 = map(int, box)
    # Draw a green rectangle around each bottle
    cv2.rectangle(output_img, (x1, y1), (x2, y2), (0, 255, 0), 2)

# Add the final count as text on the image
summary_text = f"Bottle Count: {bottle_count}"
cv2.putText(output_img, summary_text, (20, 50), 
            cv2.FONT_HERSHEY_SIMPLEX, 1.5, (0, 0, 255), 4)

# Save the resulting image
cv2.imwrite('factory_bottles_result.jpg', output_img)

print("Result image with detections has been saved as 'factory_bottles_result.jpg'")

---

Step 6: Discussion of Results and Limitations

#Discussion #Limitations #FineTuning

Result: The code successfully uses a pre-trained YOLOv8 model to identify and count standard plastic bottles in an image. The final output provides both a numerical count and a visual confirmation of the detections.

Limitations of Pre-trained Model:
1. Occlusion: If bottles are heavily clustered or hiding behind each other, the model might miss some, leading to an undercount.
2. Unusual Shapes: The model is trained on common bottles (from the COCO dataset). If your factory produces bottles of a very unique shape or color, the model's accuracy might decrease.
3. Environmental Factors: Poor lighting, motion blur (if from a fast conveyor belt), or reflections can all negatively impact detection performance.

How to Improve (Next Steps): For a real-world, high-accuracy industrial application, you should not rely on a generic pre-trained model. The best approach is Fine-Tuning. This involves:
1. Collecting Data: Take hundreds or thousands of pictures of your specific bottles in your actual factory environment*.
2. Annotating Data: Draw bounding boxes around every bottle in those images.
3. Training: Use this custom dataset to train (or "fine-tune") the YOLOv8 model. This teaches the model exactly what to look for in your specific use case, leading to much higher accuracy and reliability.

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By: @DataScienceM ✨

📌 RF-DETR Under the Hood: The Insights of a Real-Time Transformer Detection

🗂 Category: DEEP LEARNING

🕒 Date: 2025-10-31 | ⏱️ Read time: 6 min read

From rigid grids to adaptive attention, this is the evolutionary path that made detection transformers…

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🗂 Category: THE VARIABLE

🕒 Date: 2025-10-30 | ⏱️ Read time: 3 min read

A good month on TDS is one in which we get to share a wide…

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🗂 Category: DEEP LEARNING

🕒 Date: 2024-05-31 | ⏱️ Read time: 9 min read

How state of the art RNNs work

📌 Orchestrating a Dynamic Time-series Pipeline in Azure

🗂 Category: DATA ENGINEERING

🕒 Date: 2024-05-31 | ⏱️ Read time: 9 min read

Explore how to build, trigger, and parameterize a time-series data pipeline with ADF and Databricks,…

📌 Data Science Portfolios, Speeding Up Python, KANs, and Other May Must-Reads

🗂 Category: DATA SCIENCE

🕒 Date: 2024-05-30 | ⏱️ Read time: 4 min read

The stories that resonated the most with our community in the past month

#Pandas #DataAnalysis #Python #DataScience #Tutorial

Top 30 Pandas Functions & Methods

This lesson covers 30 essential Pandas functions for data manipulation and analysis, each with a standalone example and its output.

---

1. pd.DataFrame()
Creates a new DataFrame (a 2D labeled data structure) from various inputs like dictionaries or lists.

import pandas as pd
data = {'col1': [1, 2], 'col2': [3, 4]}
df = pd.DataFrame(data)
print(df)

col1  col2
0     1     3
1     2     4

---

2. pd.Series()
Creates a new Series (a 1D labeled array).

import pandas as pd
s = pd.Series([10, 20, 30, 40], name='MyNumbers')
print(s)

0    10
1    20
2    30
3    40
Name: MyNumbers, dtype: int64

---

3. pd.read_csv()
Reads data from a CSV file into a DataFrame. (Assuming a file data.csv exists).

# Create a dummy csv file first
with open('data.csv', 'w') as f:
    f.write('Name,Age\nAlice,25\nBob,30')

df = pd.read_csv('data.csv')
print(df)

Name  Age
0  Alice   25
1    Bob   30

---

4. df.to_csv()
Writes a DataFrame to a CSV file.

import pandas as pd
df = pd.DataFrame({'Name': ['Charlie'], 'Age': [35]})
# index=False prevents writing the DataFrame index to the file
df.to_csv('output.csv', index=False)
# You can check that 'output.csv' has been created.
print("File 'output.csv' created.")

File 'output.csv' created.

#PandasIO #DataFrame #Series

---

5. df.head()
Returns the first n rows of the DataFrame (default is 5).

import pandas as pd
data = {'Name': ['A', 'B', 'C', 'D', 'E', 'F'], 'Value': [1, 2, 3, 4, 5, 6]}
df = pd.DataFrame(data)
print(df.head(3))

Name  Value
0    A      1
1    B      2
2    C      3

---

6. df.tail()
Returns the last n rows of the DataFrame (default is 5).

import pandas as pd
data = {'Name': ['A', 'B', 'C', 'D', 'E', 'F'], 'Value': [1, 2, 3, 4, 5, 6]}
df = pd.DataFrame(data)
print(df.tail(2))

Name  Value
4    E      5
5    F      6

---

7. df.info()
Provides a concise summary of the DataFrame, including data types and non-null values.

import pandas as pd
import numpy as np
data = {'col1': [1, 2, 3], 'col2': [4.0, 5.0, np.nan], 'col3': ['A', 'B', 'C']}
df = pd.DataFrame(data)
df.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 3 entries, 0 to 2
Data columns (total 3 columns):
 #   Column  Non-Null Count  Dtype  
---  ------  --------------  -----  
 0   col1    3 non-null      int64  
 1   col2    2 non-null      float64
 2   col3    3 non-null      object 
dtypes: float64(1), int64(1), object(1)
memory usage: 200.0+ bytes

---

8. df.shape
Returns a tuple representing the dimensionality (rows, columns) of the DataFrame.

import pandas as pd
df = pd.DataFrame({'A': [1, 2], 'B': [3, 4], 'C': [5, 6]})
print(df.shape)

(2, 3)

#DataInspection #PandasBasics

---

9. df.describe()
Generates denoscriptive statistics for numerical columns (count, mean, std, min, max, etc.).

import pandas as pd
df = pd.DataFrame({'Age': [22, 38, 26, 35, 29]})
print(df.describe())

Age
count   5.000000
mean   30.000000
std     6.363961
min    22.000000
25%    26.000000
50%    29.000000
75%    35.000000
max    38.000000

---

10. df.columns
Returns the column labels of the DataFrame.

import pandas as pd
df = pd.DataFrame({'Name': [], 'Age': [], 'City': []})
print(df.columns)

Index(['Name', 'Age', 'City'], dtype='object')

---

11. df.dtypes
Returns the data type of each column.

import pandas as pd
df = pd.DataFrame({'Name': ['Alice'], 'Age': [25], 'Salary': [75000.50]})
print(df.dtypes)

Name       object
Age         int64
Salary    float64
dtype: object

---

12. Selecting a Column
Select a single column, which returns a Pandas Series.

import pandas as pd
data = {'Name': ['Alice', 'Bob'], 'Age': [25, 30]}
df = pd.DataFrame(data)
ages = df['Age']
print(ages)

0    25
1    30
Name: Age, dtype: int64

#DataSelection #Indexing #Statistics

---

13. df.loc[]
Access a group of rows and columns by label(s) or a boolean array.

import pandas as pd
data = {'Age': [25, 30, 35], 'City': ['NY', 'LA', 'CH']}
df = pd.DataFrame(data, index=['Alice', 'Bob', 'Charlie'])
print(df.loc['Bob'])

Age     30
City    LA
Name: Bob, dtype: object

---

14. df.iloc[]
Access a group of rows and columns by integer position(s).

import pandas as pd
data = {'Age': [25, 30, 35], 'City': ['NY', 'LA', 'CH']}
df = pd.DataFrame(data, index=['Alice', 'Bob', 'Charlie'])
print(df.iloc[1]) # Get the second row (index 1)

Age     30
City    LA
Name: Bob, dtype: object

---

15. df.isnull()
Returns a DataFrame of the same shape with boolean values indicating if a value is missing (NaN).

import pandas as pd
import numpy as np
df = pd.DataFrame({'A': [1, np.nan], 'B': [3, 4]})
print(df.isnull())

A      B
0  False  False
1   True  False

---

16. df.dropna()
Removes missing values.

import pandas as pd
import numpy as np
df = pd.DataFrame({'A': [1, np.nan, 3], 'B': [4, 5, 6]})
cleaned_df = df.dropna()
print(cleaned_df)

A  B
0  1.0  4
2  3.0  6

#DataCleaning #MissingData

---

17. df.fillna()
Fills missing (NaN) values with a specified value or method.

import pandas as pd
import numpy as np
df = pd.DataFrame({'Score': [90, 85, np.nan, 92]})
filled_df = df.fillna(0)
print(filled_df)

Score
0   90.0
1   85.0
2    0.0
3   92.0

---

18. df.drop_duplicates()
Removes duplicate rows from the DataFrame.

import pandas as pd
data = {'Name': ['Alice', 'Bob', 'Alice'], 'Age': [25, 30, 25]}
df = pd.DataFrame(data)
unique_df = df.drop_duplicates()
print(unique_df)

Name  Age
0  Alice   25
1    Bob   30

---

19. df.rename()
Alters axes labels (e.g., column names).

import pandas as pd
df = pd.DataFrame({'A': [1], 'B': [2]})
renamed_df = df.rename(columns={'A': 'Column_A', 'B': 'Column_B'})
print(renamed_df)

Column_A  Column_B
0         1         2

---

20. series.value_counts()
Returns a Series containing counts of unique values.

import pandas as pd
s = pd.Series(['A', 'B', 'A', 'C', 'A', 'B'])
print(s.value_counts())

A    3
B    2
C    1
dtype: int64

#DataManipulation #Transformation

---

21. series.unique()
Returns an array of unique values in a Series.

import pandas as pd
s = pd.Series(['A', 'B', 'A', 'C', 'A', 'B'])
print(s.unique())

['A' 'B' 'C']

---

22. df.sort_values()
Sorts a DataFrame by the values of one or more columns.

import pandas as pd
data = {'Name': ['Charlie', 'Alice', 'Bob'], 'Age': [35, 25, 30]}
df = pd.DataFrame(data)
sorted_df = df.sort_values(by='Age')
print(sorted_df)

Name  Age
1    Alice   25
2      Bob   30
0  Charlie   35

---

23. df.groupby()
Groups a DataFrame using a mapper or by a Series of columns for aggregation.

import pandas as pd
data = {'Dept': ['HR', 'IT', 'HR', 'IT'], 'Salary': [70, 85, 75, 90]}
df = pd.DataFrame(data)
grouped = df.groupby('Dept').mean()
print(grouped)

Salary
Dept        
HR      72.5
IT      87.5

---

24. df.agg()
Applies one or more aggregations over the specified axis.

import pandas as pd
data = {'Dept': ['HR', 'IT', 'HR', 'IT'], 'Salary': [70, 85, 75, 90]}
df = pd.DataFrame(data)
agg_results = df.groupby('Dept')['Salary'].agg(['mean', 'sum'])
print(agg_results)

mean  sum
Dept            
HR     72.5  145
IT     87.5  175

#Aggregation #Grouping #Sorting

---

25. df.apply()
Applies a function along an axis of the DataFrame.

import pandas as pd
df = pd.DataFrame({'A': [1, 2, 3], 'B': [10, 20, 30]})
# Apply a function to double each value in column 'A'
df['A_doubled'] = df['A'].apply(lambda x: x * 2)
print(df)

A   B  A_doubled
0  1  10          2
1  2  20          4
2  3  30          6

---

26. pd.merge()
Merges two DataFrames based on a common column or index, similar to a SQL join.

import pandas as pd
df1 = pd.DataFrame({'ID': [1, 2], 'Name': ['Alice', 'Bob']})
df2 = pd.DataFrame({'ID': [1, 2], 'Role': ['Engineer', 'Analyst']})
merged_df = pd.merge(df1, df2, on='ID')
print(merged_df)

ID   Name      Role
0   1  Alice  Engineer
1   2    Bob   Analyst

---

27. pd.concat()
Concatenates (stacks) pandas objects along a particular axis.

import pandas as pd
df1 = pd.DataFrame({'A': ['A0'], 'B': ['B0']})
df2 = pd.DataFrame({'A': ['A1'], 'B': ['B1']})
concatenated_df = pd.concat([df1, df2])
print(concatenated_df)

A   B
0  A0  B0
0  A1  B1

---

28. df.pivot_table()
Creates a spreadsheet-style pivot table as a DataFrame.

import pandas as pd
data = {'Date': ['2023-01-01', '2023-01-01', '2023-01-02'],
        'City': ['NY', 'LA', 'NY'],
        'Sales': [100, 150, 120]}
df = pd.DataFrame(data)
pivot = df.pivot_table(values='Sales', index='Date', columns='City')
print(pivot)

City             LA     NY
Date                      
2023-01-01  150.0  100.0
2023-01-02    NaN  120.0

#CombiningData #PivotTable

---

29. df.set_index()
Sets one or more existing columns as the DataFrame index.

import pandas as pd
data = {'ID': ['a1', 'a2'], 'Name': ['Alice', 'Bob']}
df = pd.DataFrame(data)
df_indexed = df.set_index('ID')
print(df_indexed)

Name
ID        
a1   Alice
a2     Bob

---

30. df.reset_index()
Resets the index of the DataFrame, making the old index a new column.

import pandas as pd
data = {'Name': ['Alice', 'Bob']}
df = pd.DataFrame(data, index=['a1', 'a2'])
df_reset = df.reset_index()
print(df_reset)

index   Name
0    a1  Alice
1    a2    Bob

#Indexing #PandasTips

━━━━━━━━━━━━━━━
By: @DataScienceM ✨

📌 The History of Bodybuilding Through Network Visualization

🗂 Category: DATA SCIENCE

🕒 Date: 2024-05-30 | ⏱️ Read time: 5 min read

Constructing the Shared Podium Graph of Mr. Olympia Winners (1965-2023) using Python and Gephi.