python programming data engineering with pandas

• Loading and cleaning data:

Python

import pandas as pd

# Load the data from a CSV file
df = pd.read_csv("data.csv")

# Clean the data by removing duplicates and filling in missing values
df.drop_duplicates()
df.fillna(0)

# Save the cleaned data to a new CSV file
df.to_csv("cleaned_data.csv")

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• Exploring and analyzing data:

Python

import pandas as pd

# Load the data from a CSV file
df = pd.read_csv("data.csv")

# Explore the data by calculating statistics and creating visualizations
df.describe()
df.plot.bar()

# Analyze the data by performing statistical tests
ttest_ind(df["column1"], df["column2"])

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• Transforming and manipulating data:

Python

import pandas as pd

# Load the data from a CSV file
df = pd.read_csv("data.csv")

# Transform the data by joining two dataframes, merging two dataframes, and splitting a dataframe
df = df.join(df2)
df = df.merge(df3)
df = df.split(df["column1"])

# Save the transformed data to a new CSV file
df.to_csv("transformed_data.csv")

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• Creating data pipelines:

Python

import pandas as pd

# Create a data pipeline by loading, cleaning, transforming, and analyzing data
df = pd.read_csv("data.csv")
df.drop_duplicates()
df.fillna(0)
df.describe()
df.plot.bar()

# Save the transformed data to a new CSV file
df.to_csv("transformed_data.csv")

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• Building machine learning models:

Python

import pandas as pd
from sklearn.linear_model import LinearRegression

# Load the data from a CSV file
df = pd.read_csv("data.csv")

# Prepare the data for machine learning by transforming the data types and splitting the data into training and test sets
X = df["column1"].values.reshape(-1, 1)
y = df["column2"].values
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.25)

# Train a machine learning model on the training data
model = LinearRegression()
model.fit(X_train, y_train)

# Evaluate the machine learning model on the test data
score = model.score(X_test, y_test)

# Print the score of the machine learning model
print(score)

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These are just a few examples of Python programming for data engineering with Pandas. There are many other things that you can do with Pandas, so be sure to explore the documentation and find other examples online.