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I would like to Learn About DATA Analytics.
Where to start it?
Where I can find the concepts about analytics?
What are all the Frameworks in PYTHON used for analytics?
Which could be good for my career(PYTHON or R)
There are vast choices for data analysis in Python. There are many frameworks which ensure that you do not have to reinvent the wheel.
Some of the major of them are:
1) NumPy: It is a Python library providing easy access to arrays, matrix operations and linear algebra.(You may also consider SciPy)
2) Pandas: It is a library which provides you 2D datasets or dataframes to store data. They are handy at times.
3) Matplotlib: It is a great library for making and plotting 2D graphs. It has the ability to make graphs and histograms with just a few lines of code.
Where to start it? Where i can find the concepts about analytic?
Data analytic/analysis is a huge concepts, but a good way to start is find a problem (data to analyse) and solve it. My suggestion is to buy a book. For example if you like to use python i suggested: "Python for Data Analysis".
What are all the Frameworks in PYTHON used for analytics?
As suggested in the other answer Numpy, Pandas and Matplotlib. Furthermore, Scipy more useful for Statistical problem. Another framework really interesting is scikit-learn http://scikit-learn.org/stable/.
Ps: a good framework that include all the former package is called Anaconda
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When we plot in python we generally use plt.~ not the specific name of the object, why?
and What if I want to plot a separate graph in one code?
This is maybe not really a question of programming, but one about culture.
The pyplot API is modelled after the plotting functions in MATLAB, and therefore, it has a lot of imperative style as heritage.
However, that API is a bit clumsy at times, as you suggest. It is recommended to instead use the object oriented API: Read more in the official docs: https://matplotlib.org/api/index.html?highlight=pylab
PS.
One can also take note that there is a deprecated API called pylab as well, which is even more similar to the MATLAB environment. You can find examples of it when looking at really old tutorials on numpy and matplotlib, but it is generally phased out.... It is further described in the above linked documentation.
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I have to make two data analysis reports using descriptive statistics, making plenty of informative plots etc.
Problem is, I'm not sure what tools should I use? I started preparing one report in Jupyter Notebook using pandas, scipy.stats and matplotlib with intention to convert it somehow to pdf later on, so I can have report without code. After hour or two I realized it might not be the best idea. I even had problem with making good description of categorical data, since pandas describe() had limited functionality on this type of data.
Could you suggest me some tools that would be best in this case? I want to prepare aesthetic, informative report. It's my first time doing data analysis including preparing report.
Your report doesn't require code, as you said. So why not just type up your report on Word and include the relevant tables and plots? You can produce plots on python using matplotlib (seaborn for aesthetic plots). And as for the statistics, you do not only have to use what pandas offers. Depending on the kind of data, for example, you can use scipy and apply those functions on columns of your dataframe to generate insights.
Also check out this data analysis and visualization software called Tableau. You can quickly create some beautiful and insightful plots using this; however, there is a learning curve.
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Question:
Which libraries/packages are the most efficient (in terms of memory use and speed) to analyze multilayer networks?
Desired answer:
Ideally, the answer to this question would involve using each of these libraries to create a pre-defined network and calculate a few centrality measures (e.g. page rank), benchmarking the amount of time and RAM memory each library uses. Something similar to this benchmark here.
List of packages
I'm particularly interested in libraries/packages in R or Python. I list below some the packages I was recommended, but feel free to add others to the list:
Libraries in Python:
Pymnet - pure python
NetworkX - pure python
graph-tool - python and C++
mammult - python and C
Libraries in R:
multinet - R and C++
multiplex - pure R
multigraph - pure R
MuxViz - pure R.
obs. I've only tried MuxViz and I quite like it. It's very easy to use and understand but I find it could be faster.
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Does anybody know if there's a way to make circos-style plots with matplotlib python package, or any other python library? They don't have to be as nice looking as the example.
As far as I know, there is no direct functionality for this. If I were to create this functionality, I would use polar plots as the starting point, then I would create a transformation to convert data positions along the circular axis to polar coordinates.
Yann's idea is great.And more, circos is developed by Perl with GD and other modules, and then output SVG (or png). Python can also do this if you want to create new wheel :)
As I know if you wanna circos style images (circular style) by matplotlib, you'd better write raw python code directly :
learn basic SVG grammar
use python to generate the basic layout
design your image elements
computing and transforming your data coordinates; you'd better think it in polar coordinates (I did the same things but without open source.)
output your elements.
If you like, join circos's google group to discuss: http://groups.google.com/group/circos-data-visualization
It seems that Biopython's module GenomeDiagram has similar functionality, although it is designed for plotting genomic data, but not any data, as in circos, and the output won't be that nice.
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I'm using Python to process CSV files filled with data that I want to run calculations on, and then graph. I'm looking for a library to use that I can send processed CSV information to, or a dict of some sort, and then choose different graphing styles with.
Does anyone have any recommendations?
I'm personally using matplotlib and am very happy with it.
Matplotlib and Gnuplot.py are popular choices. I've used both.
For client-side charts Open Flash Chart or Google Charts Tools.
I've been using matplotlib for about 3 years now to plot experimental data. Before I was using Excel and that was just a pain. I've been happy with matplotlib ever since. It's great and very powerful.
There is pychart or PyCha, both of which I have not used but have been considering myself.
It depends on the kind of graph you want,
most of the time, i'm using matplotlib but sometimespydot is good.
The various layout algorithms are good for me when dealing with huge graphs