## Diffusion via a numpy array with a list of arrays

Given an adjacency list: adj_list = [array([0,1]),array([0,1,2]),array([0,2])] And an array of indices, ind_arr = array([0,1,2]) Goal: A = np.zeros((3,3)) for i in ind_arr: A[i,list(adj_list[x])] = 1.0/float(adj_list[x].shape[0]) Currently, I have wr

## How can I load data from a text file and put it in a dictionary?

I have a data file where the first 4 csv's are floats, and the last value is a string that represents a label for that row .5, .3, .2, .1, FAA .2., .3, .5., .2, FXX .5., .3, .2 , .9, FXX .3, .3, .9, .3, FCA I want to load the file into a numpy array

## Python read the file in a certain format

I have files with a certain format as follows: 36.1 37.1 A: Hi, how are you? 39.1 40.1 B: I am ok! I am using numpy.loadtxt() to read this file with dtype = np.dtype([('start', '|S1'), ('end', 'f8'),('person','|S1'),('content','|S100')]) The first 3

## Python - An Effective Way to Add Lines to a Dataframe

From this question and others it seems that it is not recommended to use concat or append to build a pandas dataframe because it is recopying the whole dataframe each time. My project involves retrieving a small amount of data every 30 seconds. This

## Generation of Gaussian 3D data

I'm trying to generate a 3D distribution, where x, y represents the surface plane, and z is the magnitude of some value, distributed over a range. I'm looking at numpy's multivariate_normal, but it only lets me get a number of samples. I'd like the a

## How can I find a tuple of a group of tuples with the values ​​closest to a given tuple?

I'm working with python and have a dict where the keys are tuples with 3 values each. I'm computing another tuple with 3 values, and I want to find the tuple in the keys of the dict with the closest values to this newly computed tuple. How should I g

## Sort the most common combinations of two columns in descending order

I have dataframe that looks like this +---+---+--- | A| B| C| +---+---+--- | 1| 3| 1| | 2| 1| 1| | 2| 3| 1| | 1| 2| 1| | 3| 1| 1| | 1| 2| 1| | 2| 1| 1| | 1| 3| 1| | 1| 2| 1| +---+---+--- I want to reduce the data to only the most frequent combination

## Construct a numpy (matrix) array from multiple dataframes

I have several dataframes which have the same look but different data. DataFrame 1 bid close time 2016-05-24 00:00:00 NaN 2016-05-24 00:05:00 0.000611 2016-05-24 00:10:00 -0.000244 2016-05-24 00:15:00 -0.000122 DataFrame 2 bid close time 2016-05-24 0

## How to merge 2 numpy tables?

I feel like there is some documentation I am missing, but I can't find anything on this specific example - everything is just about concatenating or stacking arrays. I have array x and array y both of shape (2,3) x = [[1,2,3],[4,5,6]] y = [[7,8,9],[1

## compare two numpy arrays and add the same lines

I have two large data files, one with two columns and one with three columns. I want to select all the rows from the second file that are contained in the fist array. My idea was to compare the numpy arrays. Let's say I have: a = np.array([[1, 2, 3],

Have a data in such format in .txt file: UserId WordID 1 20 1 30 1 40 2 25 2 16 3 56 3 44 3 12 What I'm looking for- some function that can give the result grouping for every userid creating a list of wordid: [[20, 30, 40], [25, 16], [56, 44, 12]] Wh

## Np.Argwhere to produce numbers

I am working on price weighted indexes for a class and although it is a very simple calculation by hand I figured it would be good practice for my novice python skills. Edit So this is the code that I am working with now StockBPrice = np.array([35.1,

## Reshape the table in a Python square table

I have an array of numbers whose shape is 26*43264. I would like to reshape this into an array of shape 208*208 but in chunks of 26*26. [[ 0, 1, 2, 3, 4, 5, 6, 7, 8, 9], [10,11,12,13,14,15,16,17,18,19]] becomes something like: [[0, 1, 2, 3, 4], [10,1

## Global Seed for multiple Numpy imports

Assume that I have a Python project structure as: main.py which imports random_initialization.py main.py which imports sample_around_solution.py Both random_initialization and sample_around_solution.py import numpy. Now, random_initialization starts

## IndexError: too many clues. Numpy table with 1 row and 2 columns

When I try to get just the first element of an array like this import numpy a = numpy.array([1,2]) a[:,0] I get this error --------------------------------------------------------------------------- IndexError Traceback (most recent call last) <ipyth

## Python numpy array: incorrect result when mixing int32 and int8

I saw a very strange behavior in numpy array, when I mixed int32 and int8 arrays in a simple operation, the int32 array element ct[4,0] seems to have become 8bit when taking the result of += dleng[4]*4: import numpy as np In[3]: ct = np.zeros((6,1),

## Error while installing Numpy

When install pandas, it requires numpy to be installed and on installing it gives following error: Processing numpy-1.9.1.zip Writing c:\cygwin64\tmp\easy_install-4x5clr\numpy-1.9.1\setup.cfg Running numpy-1.9.1\setup.py -q bdist_egg --dist-dir c:\cy

## Numpy: vector of indices and value

I guess I'm having a slow day and can't figure this one out. I have an m x n numpy array and want to convert it to a vector where each element is a 3 dimensional vector containing the row number, column number and value of all the elements in the arr

## Average time average of different lengths

I have a number of lists (time series) dictionary = {'a': [1,2,3,4,5], 'b': [5,2,3,4,1], 'c': [1,3,5,4,6]} that I would like to average on another: merged = {'m': [2.33,2.33,3.66,4.0,4.0]} Is there a smart way to find this? What if the lists have dif

## Get the index of the element closest to the given value

This question already has an answer here: Find nearest value in numpy array 11 answers The given value is 6.6. But the value 6.6 is not in the array (data below). But the nearest value to the given value is 6.7. How can I get this position? import nu

## Setting the value of the numpy array according to several criteria

I am trying to set the values in a numpy array to zero if it is equivalent to any number in a list. Lets consider the following array a = numpy.array([[1, 2, 3], [4, 8, 6], [7, 8, 9]]) I want to set multiple elements of a which are in the list [1, 2,

## numpy matrix of time (24) and day (365)

I have two vectors; one for hours in the day [1,2,3,...,24], and the second for days in the year [1,2,3,4,5,6,...,365] I would like to construct a matrix of 24*365 cells, 24 rows and 365 columns. Something like: a = [(1,24),(2,24),(3,24),(4,24),(5,24

## Python representation and integer (zero leading problem)

This is similar to a question asked on the programming Stack Exchange: https://softwareengineering.stackexchange.com/questions/158247/binary-representation-in-python-and-keeping-leading-zeros Essentially, I have some numbers that I keep track of in h

## Get CDF of numpy arrays of varying size in Python using the same bins?

I'd like to make a set of comparable empirical CDFs for a few numpy arrays (each of different length) and store these in a pandas dataframe: a = scipy.randn(100) b = scipy.randn(500) # ECDF from statmodels cdf_a = ECDF(a) cdf_b = ECDF(b) The problem

## Error while freezing the code pandas / NumPy 1.7.0 with cx_Freeze

I am trying to freeze a Python script with cx_Freeze. The script makes use of pandas. When I run the executable created by cx_Freeze, I get the following Traceback: [...] File "C:\Python27\lib\site-packages\pandas\__init__.py", line 6, in <mo

## Python Floating Point Precision Format Specifier

Let's say I have some 32-bit numbers and some 64-bit numbers: >>> import numpy as np >>> w = np.float32(2.4) >>> x = np.float32(4.555555555555555) >>> y = np.float64(2.4) >>> z = np.float64(4.555555555555555) I

## Is it possible to check if NumPy tables share the same data?

My impression is that in NumPy, two arrays can share the same memory. Take the following example: import numpy as np a=np.arange(27) b=a.reshape((3,3,3)) a[0]=5000 print (b[0,0,0]) #5000 #Some tests: a.data is b.data #False a.data == b.data #True c=n

## Run a python script (with a numpy dependency) from Java

In a java application I need to use a specific image processing algorithm that is currently implemented in python. What would be the best approach, knowing that this script uses the Numpy library ? I alreayd tried to compile the script to java using

## How can I pass large tables between numpy and R?

I'm using python and numpy/scipy to do regex and stemming for a text processing application. But I want to use some of R's statistical packages as well. What's the best way to pass the data from python to R? (And back?) Also, I need to backup the arr

## Incrementation of die subsets in Python

I want to increment a small subsection (variable) of an matrix [illustrative code below] - but running over them by loops seems sloppy and inelegant -- and I suspect is the slowest way to do this calc. One of the ideas I had was to create another arr