dipencsv 0.1.3

A beginner-friendly CSV analysis and ML data preparation toolkit

DipenCSV 🐼

DipenCSV is to pandas what seaborn is to matplotlib — a friendlier, higher-level API.

A beginner-friendly CSV analysis and ML data preparation toolkit built on top of pandas. Helps students and small teams clean, analyze, and prepare CSV data for machine learning — without needing to know pandas.

Who is it for?

• 🎓 Students cleaning data for ML assignments
• 🚀 Small startups needing quick data insights
• 👨‍💻 Developers who work with CSVs occasionally
• 📊 Anyone who finds pandas too complex

Installation

pip install dipencsv

Quick Start

from dipencsv import Data

data = Data("your_file.csv")
data.magic()  # one click everything

Full ML Workflow in 15 lines

from dipencsv import Data
from sklearn.ensemble import RandomForestClassifier

data = Data("titanic.csv")
data.clean()
data.drop(["passengerid", "name", "ticket", "cabin"])
data.encode("sex")
data.encode("embarked")
data.remove_outliers("fare")
data.normalize("fare")
data.normalize("age")

train, test = data.split(test_size=0.2)

X_train = train.drop("survived", axis=1)
y_train = train["survived"]
X_test = test.drop("survived", axis=1)
y_test = test["survived"]

model = RandomForestClassifier()
model.fit(X_train, y_train)
print(f"Accuracy: {model.score(X_test, y_test):.2f}")
# Accuracy: 0.76

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All Commands

Loading

data = Data("file.csv")                        # auto mode
data = Data("bigfile.csv", stream=True)        # force stream mode
data = Data("file.csv", auto_mode=False)       # force pandas mode

Properties

print(data.columns)   # list of column names
print(data.shape)     # (rows, columns)

Understanding Data

data.summary()    # rows, cols, types, missing values
data.report()     # duplicates, missing %, basic stats
data.explain()    # trends, issues, suggestions
data.magic()      # ⭐ one click full analysis

Cleaning

data.clean()                        # auto clean (safe mode)
data.clean(strategy="aggressive")   # drop rows with missing values
data.drop("column")                 # drop one column
data.drop(["col1", "col2"])         # drop multiple columns
data.rename("old_name", "new_name") # rename column
data.fill("column", value)          # fill missing values manually

⚠️ After clean(), column names become lowercase with underscores. Always check: print(data.columns)

ML Preparation

data.encode("gender")           # text → numbers (label encoding)
data.normalize("age")           # scale to [0, 1]
data.standardize("salary")      # scale to mean=0, std=1
data.remove_outliers("price")   # remove extreme values (IQR method)
train, test = data.split(test_size=0.2)  # train/test split

Analytics

data.mean("salary")
data.median("age")
data.max("salary")
data.min("salary")
data.count("city")
data.correlation("age", "salary")
data.correlation_matrix()
data.distribution("salary")
data.outliers("salary")
data.describe()
data.value_counts("city")
data.group_mean("city", "salary")

Querying

data.first()              # first 5 rows
data.first(10)            # first 10 rows
data.last()               # last 5 rows
data.sort("age")          # sort ascending
data.sort("age", asc=False)  # sort descending
data.find("city", "Bangalore")  # find rows by value

Intelligence

data.ask("average salary")
data.ask("top 10 salary")
data.ask("highest salary by city")
data.ask("lowest age")
data.ask("count city")
data.explain()
data.magic()

Export

data.export("output.csv")    # CSV
data.export("output.json")   # JSON
data.export("output.xlsx")   # Excel (requires openpyxl)

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Stream Mode (Big Files)

DipenCSV auto detects file size and switches to stream mode for files > 500MB:

data = Data("hugefile.csv")          # auto detects
data = Data("hugefile.csv", stream=True)  # force stream

Supported in stream mode: mean(), max(), min(), count(), group_mean(), filter(), top_n()

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Smart Error Handling

data.mean("salry")
# ❌ Column 'salry' not found.
# 💡 Did you mean:
#   - salary
#   - salary_usd

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Common Issues

KeyError after clean()

data.clean()
print(data.columns)      # check actual column names
data.mean("salary")      # use lowercase

Excel export failing

pip install openpyxl

Big file crashes

data = Data("bigfile.csv", stream=True)

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Dependencies

• pandas >= 1.5.0
• scikit-learn >= 1.0.0
• openpyxl >= 3.0.0 (optional, Excel export)

Documentation

Doc	What it covers
Getting Started	installation, quick start
Loading Data	Data(), auto mode, stream mode
Properties	columns, shape
Understanding Data	summary, report, explain, magic
Cleaning Data	clean, drop, rename, fill
ML Preparation	encode, normalize, split etc
Analytics	mean, correlation, outliers etc
Query	sort, find, first, last
Intelligence	ask, explain, magic
Stream Mode	big file handling
Export	csv, json, xlsx
Examples	real world examples
FAQ	common student questions
ML Workflow	full end-to-end ML example

License

MIT License

Author

Built by Dipendra — a CS student who wanted pandas to be less painful.