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Easy-to-use data analysis / manipulation framework for humans
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LICENSE V1.10 Aug 26, 2019

This open source framework fluently implements your ideas for data mining.

DaPy - Enjoy the Tour in Data Mining



DaPy is a data analysis library designed with ease of use in mind, which lets you smoothly implement your thoughts by providing well-designed data structures and abundant professional ML models. There has been a lot of famous data operation modules like Pandas already, but there is no module, which

  • can write the codes in Chain Programming;
  • can quickly do simple feature engineering with simple APIs;
  • can easily operate the data row by row;
  • can show the log of each steps on console like MySQL.


This example simply shows the characters of DaPy of chain programming, working log and simple feature engineering method. Our goal in this example is to train a classifier for Iris classification task. Detail information can be read from here.

Why I need DaPy?

We already have abundant of great libraries for Data Science like Numpy and Pandas, why we need DaPy?

The answer is DaPy is designed for Data Analysis, not for coders. In DaPy, users only need to focus on their thought of handling data, and pay less attention to coding tricks.

For example, while manipulating data by rows fits for people's habits, it is not a good idea in Pandas. Because Pandas is build for operate time series data, it is forbidden to operate rows from DataFrame.iterrows(). However, DaPy relies on the concept of "views" to solve this problem, making it easy to process data in rows in a way that suits people's habits.

>>> import DaPy as dp
>>> sheet = dp.SeriesSet({'A': [1, 2, 3], 'B': [4, 5, 6]})
>>> for row in sheet:
		print(row.A, row[0])   # get the value by column name or index
		row[1] = 'b'   # assign value by index
1, 1
2, 2
3, 3
 A | B
 1 | b 
 2 | b 
 3 | b 
>>> row0 = sheet[0]   # get row view object 
>>> row0
[1, 'b']
>>> sheet.append_col(series=[7, 8, 9], variable_name='newColumn') # operate the sheet
 A | B | newColumn
 1 | b |     7     
 2 | b |     8     
 3 | b |     9     
>>> row0
[1, 'b', 7]

Features of DaPy

We hope DaPy is an user-friendly tool. Therefore, we effort to the design of APIs in DaPy in order to let you quickly adept it and use it flexibly. Here are just a few of things that make DaPy simple:

  • Variety of ways to visualize data in CMD
  • 2D data sheet structures following Python syntax habits
  • SQL-like APIs to process data
  • Variety functions for preprocessing and feature engineering
  • Flexible IO tools for loading and saving data (e.g. Website, Excel, Sqlite3, SPSS, Text)
  • Built-in basic models (e.g. Decision Tree, Multilayer Perceptron, Linear Regression, ...)

Also, we hope it can be used in some real-world tasks, thereby we are keeping an eye on its efficiency. Although DaPy is implemented by pure Python, it has comparable efficiency to some exists libraries. Following dialog shows a testing result and the data had 4.32 million rows and 7 columns.

Performance Test

Following are the standards of performance test.

  • Task 1: load

    Libraries have to load the original data from a CSV format file. In this CSV file, it has different columns with different data types. The libraries must have the ability to automatically predict the best matched data type then transfer the values. We recorded the time consumption of each library spent on the task. The commands we used are listed as bellow.

    >>> pandas.readcsv(addr) 
    >>> numpy.genfromtxt(addr, dtype=None, delimiter=',', encoding=None, names=True)
  • Task 2: Traverse

    Libraries have to traverse each row of the data loaded in Task1. We recorded the time consumption of each library spent on the task. The commands we used are listed as bellow.

    >>> for row in pd_DataFrame.itertuples():
    >>> for row in np_Ndarray:
    >>> for row in dp_SeriesSet.iter_rows():
  • Task 3: Sort

    Libraries have to sort the records from the data loaded in Task 1 by one column named "Price". We recorded the time consumption of each library spend on the task. The commands we used in this task are listed as bellow.

    >>> pd_DataFrame.sort_values(by='Price')
    >>> np_Ndarray.sort(axis=0, order='Price')
    >>> dp_SeriesSet.sort('Price')
  • Task 4: Query

    Libraries have to select the records that the keyword "Price" is greater than 99999. We recorded the time consumption of each library spent on the task. The commands we used are listed as bellow.

    >>> pd_DataFrame.query('Price >= 99999')
    >>> numpy.extract(tuple(_['Price'] > 99999 for _ in np_Ndarray), np_Ndarray)
    >>> dp_SeriesSet.query('Price >= 99999', limit=None)
  • Task 5: Groupby

    Libraries have to separate the records into groups according to the keyword of "Date", than calculate the mean of each column for each subset. Because numpy.ndarray doesn't support the groupby operation, Numpy skips this task. We recorded the time consumption of each library spent on the task. The commands we used are listed as bellow.

    >>> pd_DataFrame.groupby('Date')[['Price', 'Volume', 'Token', 'LastToken', 'LastMaxVolume']].mean()
    >>> dp_SeriesSet.groupby('Date', np.mean, apply_col=['Price', 'Volume', 'Token', 'LastToken', 'LastMaxVolume'])
  • Task 6: Save

    Libraries have to save their data into a CSV format file. We recorded the time consumption of each library spent on the task. The commands we used are listed as bellow.

    >>> pd_DataFrame.to_csv('test_Pandas.csv', index=0)
    >>> np.savetxt('test_numpy.csv', np_Ndarray, delimiter=',', fmt='%s%s%s%s%s%s%s')


The latest version 1.10.1 had been updated to PyPi.

pip install DaPy

Some of functions in DaPy depend on requirements.

  • xlrd: loading data from .xls file【Necessary】
  • xlwt: export data to a .xls file【Necessary】
  • repoze.lru: speed up loading data from .csv file【Necessary】
  • savReaderWrite: loading data from .sav file【Optional】
  • bs4.BeautifulSoup: auto downloading data from a website【Optional】
  • numpy: dramatically increase the efficiency of ML models【Recommand】


  • Load & Explore Data
    • Load data from a local csv, sav, sqlite3, mysql server, mysql dump file or xls file: sheet =
    • Display the first five and the last five records:
    • Summary the statistical information of each columns:
    • Count distribution of categorical variable: sheet.count_values('gender')
    • Find differences of the labels in categorical variables: sheet.groupby('city')
    • Calculate the correlation between the continuous variables: sheet.corr(['age', 'income'])
  • Preprocessing & Clean Up Data
    • Remove duplicate records: sheet.drop_duplicates(col, keep='first')
    • Use linear interpolation to fill in NaN : sheet.fillna(method='linear')
    • Remove the records which contains more than 50% variables are NaN: sheet.dropna(axis=0, how=0.5)
    • Remove some meaningless columns (e.g. ID): sheet.drop('ID', axis=1)
    • Sort records by some columns: sheet = sheet.sort('Age', 'DESC')
    • Merge external features from another table: sheet.merge(sheet2, left_key='ID', other_key='ID', keep_key='self', keep_same=False)
    • Merge external records from another table: sheet.join(sheet2)
    • Append records one by one: sheet.append_row(new_row)
    • Append new variables one by one: sheet.append_col(new_col)
    • Get parts of records by index: sheet[:10, 20: 30, 50: 100]
    • Get parts of columns by column name: sheet['age', 'income', 'name']
  • Feature Engineering
    • Transfer a date time into categorical variables: sheet.get_date_label('birth')
    • Transfer numerical variables into categorical variables: sheet.get_categories(cols='age', cutpoints=[18, 30, 50], group_name=['Juveniles', 'Adults', 'Wrinkly', 'Old'])
    • Transfer categorical variables into dummy variables: sheet.get_dummies(['city', 'education'])
    • Create higher-order crossover terms between your selected variables: sheet.get_interactions(n_power=3, col=['income', 'age', 'gender', 'education'])
    • Introduce the ranks of each records: sheet.get_ranks(cols='income', duplicate='mean')
    • Standardize some normal continuous variables: sheet.normalized(col='age')
    • Special processing for some special variables: sheet.normalized('log', col='salary')
    • Create new variables by some business logical formulas: sheet.apply(func=tax_rate, col=['salary', 'income'])
    • Difference process to make time-series stable: DaPy.diff(sheet.income)
  • Developing Models
    • Choose a model and initialize it: m = MLP(), m = LinearRegression(), m = DecisionTree() or m = DiscriminantAnalysis()
    • Train the model parameters:, Y_train)
  • Model Evaluation
    • Evaluate model with parameter tests:
    • Evaluate model with visualization: m.plot_error() or DecisionTree.export_graphviz()
    • Evaluate model with test set: DaPy.methods.Performance(m, X_test, Y_test, mode).
  • Saving Result
    • Save the model:
    • Save the final dataset:


✔️ = Done 🏃 = In Development ​ 📆 = Put On the Agenda 🤔 = Not Sure

  • Data Structures

    • DataSet (3-D data structure) ✔️
    • Frame (2-D general data structure)​ ✔️
    • SeriesSet (2-D general data structure) ✔️
    • Matrix (2-D mathematical data structure) ✔️
    • Row (1-D general data structure) ✔️
    • Series (1-D general data structure) ✔️
    • TimeSeries (1-D time sequence data structure)​ 🏃
  • Statistics

    • Basic Statistics (mean, std, skewness, kurtosis, frequency, fuantils)​ ✔️

    • Correlation (spearman & pearson) ✔️

    • Analysis of variance ✔️

    • Compare Means (simple T-test, independent T-test) ✔️

  • Operations

    • Beautiful CRUD APIs (create, Retrieve, Update, Delete) ✔️
    • Flexible I/O Tool(supporting multiple source data for input and output) ✔️
    • Dummy Variables (auto parse norminal variable into dummy variable) ✔️
    • Difference Sequence Data ✔️
    • Normalize Data (log, normal, standard, box-cox)✔️
    • Drop Duplicate Records ✔️
    • Group By (analysis the dataset under controlling a group variable)✔️
  • Methods

    • LDA (Linear Discriminant Analysis) ✔️
    • LR (Linear Regression) ✔️
    • ANOVA (Analysis of Variance) ✔️
    • MLP (Multi-Layers Perceptron) ✔️
    • DT (Decision Tree)✔️
    • K-Means 🏃
    • PCA (Principal Component Analysis) 🏃
    • ARIMA (Autoregressive Integrated Moving Average) 📆
    • SVM ( Support Vector Machine) 🤔
    • Bayes Classifier 🤔
  • Others

    • Manual 🏃
    • Example Notebook 🏃
    • Unit Test 🏃



  • V1.10.1 (2019-08-22)

    • Added SeriesSet.update(), update some values of specific records;
    • Added BaseSheet.tolist() and BaseSheet.toarray(), transfer your data to list or numpy.array;
    • Added BaseSheet.query(), select records with a python statement in string;
    • Added SeriesSet.dropna(), drop rows or variables which contain NaN;
    • Added SeriesSet.fillna(), fill missing values in the dataset with constant value or linear model;
    • Added SeriesSet.label_date(), transfer a datetime object to several columns;
    • Added DaPy.Row, a view of a row record of the original data;
    • Added DaPy.methods.DecitionTree, classifier implemented with C4.5 algorithm;
    • Added DaPy.methods.SignTest, supported some of sign test algorithms;
    • Refactored the structure of DaPy.core.base package;
    • Optimized BaseSheet.groupby(), 18 times faster than ever before;
    • Optimized, 14 times faster than ever before;
    • Optimized BaseSheet.sort(), 2 times faster than ever before;
    • Optimized, 1.6 times faster than ever before to saving data to a .csv;
    • Optimized, 10% faster than ever before to loading data from .csv;
  • V1.9.2 (2019-04-23)

    • Added BaseSheet.groupby(), regroup your observations with specific columns;
    • Added DataSet.apply(), mapping a function to the dataset by axis;
    • Added DataSet.drop_duplicates(), automatically dropout the duplicate records in the dataset;
    • Added DaPy.Series, a new data structure to obtain a sequence of data;
    • Added DaPy.methods.Performance(), automatically testify the performance of ML models;
    • Added DaPy.methods.Kappa(), calculate the Kappa index with a confusing matrix;
    • Added DaPy.methods.ConfuMat(), calculate the Confusing matrix with your result;
    • Added DaPy.methods.DecitionTree(), implement the C4.5 decision tree algorithm;
    • Refactored the structure of DaPy.core.base package;
    • More on, supports new keywords "limit" and "columns";
  • V1.7.2 Beta (2019-01-01)

    • Added get_dummies() , supports to auto process norminal variables;
    • Added show_time attribute, auto timer for DataSet object;
    • Added boxcox() , supports Box-Cox transformation to a sequence data;
    • Added diff(), supports calculate the differences to a sequence data;
    • Added DaPy.methods.LDA, supports DIscriminant Analysis on two methods (Fisher & Linear);
    • Added row_stack(), supports to combine multiple data structures with out DataSet;
    • Added Row structure for handling a record in sheet;
    • Added report attribute to all classes in methods, you can read a statistical report after training a model;
    • More on read(), supports to auto parse data from a web address;
    • More on SeriesSet.merge(), more options when we merge to SeriesSets;
    • Rename DataSet.pop_miss_value() into DataSet.dropna();
    • Refactored methods, more stable and more scalable in the future;
    • Refactored methods.LinearRegression, it can prepare a statistic report for you after training;
    • Refactored, 5 times faster and more pythonic API design;
    • Refactored BaseSheet.replace(), 20 times faster and more pythonic API design;
    • Supported Python 3.x platform;
    • Fixed a lot of bugs;
  • V1.5.3 (2018-11-17)

    • Added select(), quickly access partial data with some conditions;
    • Added delete(), delete data along the axis from a un-DaPy object;
    • Added column_stack(), merging several un-DaPy objects together;
    • Added P() & C() , calculating permutation numbers and combination numbers;
    • Added new syntax, therefore users can view values in a column with statement as data.title.
    • Optimized, supported external saving data types: html and SQLite3;
    • Refactored BaseSheet, less codes and more flexsible in the future;
    • Refactored, more stable and more flexsible in the future;
    • Rewrite a part of basic mathematical functions;
    • Fixed some bugs;
  • V1.4.1 (2018-08-19)

    • Added replace() for high-speed transering your data;
    • Optimized the speed in reading .csv file;
    • Refactored the methods.MLP, customized with any layers, any active functions and any cells now;
    • Refactored the Frame and SeriesSet to improve the efficiency;
    • Supported to initialize Pandas and Numpy data structures;
    • Fixed some bugs;
  • V1.3.3 (2018-06-20)

    • Added methods.LinearRegression and methods.ANOVA ;
    • Added io.encode() for better adepting to Chinese;
    • Optimized SeriesSet.__repr__() and Frame.__reprt__() to show data in beautiful way;
    • Optimized the Matrix, so that the speed in calculating is two times faster;
    • More on read() , supports external file as: Excel, SPSS, SQLite3, CSV;
    • Renamed DataSet.read_col(), DataSet.read_frame(), DataSet.read_matrix() by;
    • Refactored the DataSet, which can manage multiple sheets at the same time;
    • Refactored the Frame and SeriesSet, delete the attributes' limitations;
    • Removed DaPy.Table;
  • V1.3.2 (2018-04-26)

    • Added more useful functions for DaPy.DataSet;
    • Added a new data structure called DaPy.Matrix;
    • Added some mathematic formulas (e.g. corr, dot, exp);
    • Added Multi-Layers Perceptrons to DaPy.machine_learn;
    • Added some standard dataset;
    • Optimized the loading function significantly;
  • V1.3.1 (2018-03-19)

    • Added the function which supports to save data as a csv file;
    • Fixed some bugs in the loading data function;
  • V1.2.5 (2018-03-15)

    • First public beta version of DaPy!


Copyright (C) 2018 - 2019 Xuansheng Wu

This program is free software: you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation, either version 3 of the License, or (at your option) any later version.

This program is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details.

You should have received a copy of the GNU General Public License along with this program. If not, see https:\\licenses.# datapy A light Python library for data processing and analysing.

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