TensorFlow vs Pandas A Clear Comparison for Beginners

import pandas as pd

# Read data
df = pd.read_csv("sales.csv")

# Inspect
print(df.head())
print(df.info())
print(df.describe())

# Clean
df = df.dropna()
df["revenue"] = df["price"] * df["qty"]

# Group and aggregate
summary = df.groupby("region")["revenue"].sum()
print(summary)

# Save
summary.to_csv("revenue_by_region.csv")

# A typical Pandas workflow — before any ML

import pandas as pd
from sklearn.model_selection import train_test_split

# 1. Load
df = pd.read_csv("customers.csv")

# 2. Clean
df = df.drop_duplicates()
df["age"] = df["age"].fillna(df["age"].median())

# 3. Feature engineering
df["tenure_months"] = (
    pd.Timestamp.today() - pd.to_datetime(df["signup_date"])
).dt.days // 30

# 4. Encode categoricals
df = pd.get_dummies(df, columns=["plan"], drop_first=True)

# 5. Split
X = df.drop(columns=["churned"])
y = df["churned"]
X_train, X_test, y_train, y_test = train_test_split(
    X, y, test_size=0.2, random_state=42
)

# Now X_train is ready for a model — TensorFlow, scikit-learn, etc.

import tensorflow as tf

# Build a simple feedforward network
model = tf.keras.Sequential([
    tf.keras.layers.Dense(64, activation="relu", input_shape=(10,)),
    tf.keras.layers.Dense(32, activation="relu"),
    tf.keras.layers.Dense(1, activation="sigmoid")
])

# Compile
model.compile(
    optimizer="adam",
    loss="binary_crossentropy",
    metrics=["accuracy"]
)

# Train
history = model.fit(
    X_train, y_train,
    validation_split=0.2,
    epochs=20,
    batch_size=32
)

# Evaluate
loss, acc = model.evaluate(X_test, y_test)
print(f"Test accuracy: {acc:.3f}")

# A deeper example — image classification with CNNs

import tensorflow as tf
from tensorflow.keras import layers

model = tf.keras.Sequential([
    layers.Conv2D(32, (3, 3), activation="relu", input_shape=(28, 28, 1)),
    layers.MaxPooling2D((2, 2)),
    layers.Conv2D(64, (3, 3), activation="relu"),
    layers.MaxPooling2D((2, 2)),
    layers.Flatten(),
    layers.Dense(64, activation="relu"),
    layers.Dense(10, activation="softmax")
])

model.compile(
    optimizer="adam",
    loss="sparse_categorical_crossentropy",
    metrics=["accuracy"]
)

model.summary()

history = model.fit(
    x_train, y_train,
    epochs=10,
    validation_data=(x_test, y_test)
)

# Save for deployment
model.save("mnist_model.h5")

# End-to-end: Pandas prepares, TensorFlow trains

import pandas as pd
import numpy as np
import tensorflow as tf
from sklearn.model_selection import train_test_split

# 1. Pandas — load and clean
df = pd.read_csv("churn.csv")
df = df.dropna()
df = pd.get_dummies(df, drop_first=True)

# 2. Split features and target
X = df.drop(columns=["churned"]).values
y = df["churned"].values

# 3. Split train/test
X_train, X_test, y_train, y_test = train_test_split(
    X, y, test_size=0.2, random_state=42
)

# 4. TensorFlow — build and train
model = tf.keras.Sequential([
    tf.keras.layers.Dense(64, activation="relu"),
    tf.keras.layers.Dropout(0.2),
    tf.keras.layers.Dense(1, activation="sigmoid")
])

model.compile(optimizer="adam", loss="binary_crossentropy", metrics=["accuracy"])
model.fit(X_train, y_train, epochs=20, batch_size=32, validation_split=0.2)

# 5. Evaluate
loss, acc = model.evaluate(X_test, y_test)
print(f"Test accuracy: {acc:.3f}")

# When you don't need TensorFlow — Pandas + scikit-learn

import pandas as pd
from sklearn.ensemble import RandomForestClassifier
from sklearn.model_selection import train_test_split
from sklearn.metrics import accuracy_score

df = pd.read_csv("churn.csv").dropna()
df = pd.get_dummies(df, drop_first=True)

X = df.drop(columns=["churned"])
y = df["churned"]

X_train, X_test, y_train, y_test = train_test_split(
    X, y, test_size=0.2, random_state=42
)

model = RandomForestClassifier(n_estimators=100)
model.fit(X_train, y_train)

preds = model.predict(X_test)
print("Accuracy:", accuracy_score(y_test, preds))

# Rule of thumb:
# Tabular data → scikit-learn is usually enough
# Images, text, audio, large neural nets → TensorFlow