Data-Science-Python | Data Science analysis and visualization using Python | Machine Learning library

by GeorgeSeif Python Version: Current License: No License

X-Ray Key Features Code Snippets Community Discussions(5)Vulnerabilities Install Support

kandi X-RAY | Data-Science-Python Summary

Data-Science-Python is a Python library typically used in Institutions, Learning, Education, Artificial Intelligence, Machine Learning, Numpy, Pandas applications. Data-Science-Python has no bugs, it has no vulnerabilities and it has low support. However Data-Science-Python build file is not available. You can download it from GitHub.

A collection of data science scripts for data analysis in Python. Please also see my related repository Python Machine Learning which contains many implementations of Machine Learning algorithms including regression, classification, and clustering. The algorithms are implemented in two ways: from scratch in Python and using Scikit Learn functions.

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Data-Science-Python has a low active ecosystem.

It has 18 star(s) with 19 fork(s). There are 3 watchers for this library.

It had no major release in the last 6 months.

Data-Science-Python has no issues reported. There are no pull requests.

It has a neutral sentiment in the developer community.

The latest version of Data-Science-Python is current.

Quality

Data-Science-Python has 0 bugs and 0 code smells.

Security

Data-Science-Python has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.

Data-Science-Python code analysis shows 0 unresolved vulnerabilities.

There are 0 security hotspots that need review.

License

Data-Science-Python does not have a standard license declared.

Check the repository for any license declaration and review the terms closely.

Without a license, all rights are reserved, and you cannot use the library in your applications.

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Data-Science-Python releases are not available. You will need to build from source code and install.

Data-Science-Python has no build file. You will be need to create the build yourself to build the component from source.

Installation instructions are available. Examples and code snippets are not available.

Data-Science-Python saves you 120 person hours of effort in developing the same functionality from scratch.

It has 302 lines of code, 26 functions and 6 files.

It has low code complexity. Code complexity directly impacts maintainability of the code.

Top functions reviewed by kandi - BETA

kandi has reviewed Data-Science-Python and discovered the below as its top functions. This is intended to give you an instant insight into Data-Science-Python implemented functionality, and help decide if they suit your requirements.

Calculate the correlation coefficient
Compute the variance of x
Compute the covariance of x and y
Return the standard deviation of x
Calculate the interval range
Compute the quantile of x
Gradient of sigmoid
Sigmoid function
Gaussian function for 2d Gaussian
Compute the Gaussian log - likelihood function
Computes the principal components of the covariance matrix
Compute the covariance matrix
Normalize data
Compute the mean and variance of the data
Splits the training and test data
Shuffle the data
Compute the median of a list

Get all kandi verified functions for this library.

Data-Science-Python Key Features

No Key Features are available at this moment for Data-Science-Python.

Data-Science-Python Examples and Code Snippets

No Code Snippets are available at this moment for Data-Science-Python.

Community Discussions

Trending Discussions on Data-Science-Python

Why is this creating a second plot instead of filling the second subplot?

Why am I getting an extra bar graph in Python?

How to tell Pandas/Scikit-Learn how one field impacts predictive model

ValueError: invalid fill value with a

ValueError: Input contains NaN, infinity or a value too large for dtype('float64')

QUESTION

Why is this creating a second plot instead of filling the second subplot?

Asked 2019-Dec-21 at 09:37

So I'm following along this tutorial: https://www.analyticsvidhya.com/blog/2016/01/complete-tutorial-learn-data-science-python-scratch-2/

And I'm encountering an issue that I am having a hard time grasping. My goal is to output two subplots side-by-side, the left feeding from a temp1 dataframe, and the right from temp2 table:

temp1:

...

ANSWER

Answered 2019-Dec-21 at 09:37

temp2.plot(kind = 'bar') is a pandas built-in graph function, so use plt.bar(X, y) instead.

like this :
(I use this dataframe for example, 3 rows)

Source https://stackoverflow.com/questions/59433917

QUESTION

Why am I getting an extra bar graph in Python?

Asked 2019-Mar-29 at 01:31

I'm currently following along in my iPython notebook on a beginner-level Loan Prediction classification problem on analyticsvidhya.com.

(https://www.analyticsvidhya.com/blog/2016/01/complete-tutorial-learn-data-science-python-scratch-2/)

I'm using inline Pylab on Jupyter.

So far we've coded a pivot table and bar graphs. But when I try to plot the 2 bar graphs, I get 3 bar graphs with one of them blank.

...

ANSWER

Answered 2019-Mar-29 at 01:31

Try the following: pass the axis objects while plotting dataframes

Source https://stackoverflow.com/questions/55409183

QUESTION

How to tell Pandas/Scikit-Learn how one field impacts predictive model

Asked 2018-Jan-28 at 18:56

I am trying to create/validate a predictive model using a fictitious dataset, using Phyton with sklearn, following this tutorial.

The dataset contains information about baseball pitcher throws, and these are the most important fields:

Result (whether the player was successful/unsuccessful in throwing a strike)
Direction (whether it was a High, Medium, or Low throw)
Other fields like speed of ball, player stats, etc.

Based on the different fields, the model will attempt to predict what direction (the Direction field) a pitcher should throw in order to get a strike.

In the tutorial I am following (the link above,) this is an example of a call to the function that generates the model, in this case for logistic regression (but we could use any of the other classification techniques listed):

...

ANSWER

Answered 2018-Jan-28 at 18:56

You don't.

The whole point of doing Machine Learning is to have the machine automatically learning relationships and rules from data.

So, they way of helping the model find such relationships is to provide it as much (correct) data as possible. With enough data, a decent model should be able to generalise and find out, in your case, whether the 'Result' field is useful or not for predicting the 'Direction' outcome.

Source https://stackoverflow.com/questions/48478832

QUESTION

ValueError: invalid fill value with a

Asked 2017-Sep-02 at 18:00

I am practising on a loan prediction practise problem and trying to fill missing values in my data. I obtained the data from here. To complete this problem I am following this tutorial.

You can find the entire code (file name model.py) I am using and the data here on GitHub.

The DataFrame looks like this:

...

ANSWER

Answered 2017-Jun-13 at 07:04

It seems author of tutorial want replace NaN by values of table.

But need first create Series by unstack and set_index for align data.

First remove replacing to NaN by mean:

Source https://stackoverflow.com/questions/44450725

QUESTION

ValueError: Input contains NaN, infinity or a value too large for dtype('float64')

Asked 2017-Jun-21 at 08:27

I am practising on a loan prediction practise problem and trying to fill missing values in my data. I obtained the data from here. To complete this problem I am following this tutorial.

You can find the entire code (file name model.py) I am using and the data on GitHub.

The DataFrame looks like this:

After the last line is executed (corresponds to line 122 in the model.py file)

...

ANSWER

Answered 2017-Jun-21 at 08:27

You can use fillna:

Source https://stackoverflow.com/questions/44669921

Community Discussions, Code Snippets contain sources that include Stack Exchange Network

Vulnerabilities

No vulnerabilities reported

Install Data-Science-Python

To install all of the libraries, run the commands in the "install.txt" file. These are:.
sudo apt-get install python-pip
sudo pip install numpy scipy
sudo pip install pandas
sudo apt-get install python-matplotlib
sudo pip install -U scikit-learn
sudo pip install tabulate

Support

For any new features, suggestions and bugs create an issue on GitHub. If you have any questions check and ask questions on community page Stack Overflow .

Find more information at: