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python - How to access the ith column of a NumPy multidimensional array? - Stack Overflow
To split a 2D array into its columns the easiest approach is to transpose the array and get all the resulting rows, which, are in effect the columns of the original 2D array.
stackoverflow  howto  example  python  python2.7  get  columns  2d  array  two  dimension  rows  transpose  method  numpy  pandas 
9 days ago by racl101
numpy.random.randint — NumPy v1.14 Manual
useful method for getting either an array or a 2D matrix of randomly generated integers between two numbers.

e.g.

Get a Numpy array of 100 numbers between 0 and 50

arr1 = np.random.randint(0,50, 100)

Now, suppose you want that same data in a 5 by 10 matrix. You can do this like so:

matrix_1 = arr1.reshape(5,10)
numpy  documentation  guide  reference  random  randint  method  generate  integers  between  minimum  maximum  rows  columns  shape  howto  example 
9 days ago by racl101
pandas.DataFrame.dropna — pandas 0.13.1 documentation
Convenient method for dropping rows or columns in a Pandas dataframe containing over a threshhold ( a limit) of non NaN (non existent, not available, null) values in a dataframe.

Note that the threshold argument is to specify non-NaN values. To use it to limit the number of NaN values, you need to know the number of columns in the dataframe beforehand like so: thresh=(len(df.columns)-limit_of_nan_values_per_row)
python  python2.7  pandas  dropna  method  filter  drop  rows  colums  nan  notavailable  values  dataframe  documentation  guide  reference 
10 days ago by racl101
python - Selecting columns in a pandas dataframe - Stack Overflow
Really helpful feature of Pandas that allows me to slice a section dataframe by specifying two columns. That is get all the rows between two columns. Works pretty well too.
stackoverflow  python  python2.7  howto  example  slice  vertically  columns  dataframe  guide  reference  loc  method 
11 days ago by racl101
numpy.column_stack — NumPy v1.14 Manual
If you're looking to combine different multidimensional numpy arrays (with several rows and columns) into one large array of mixed types, don't use numpy.column_stack method because it will convert all of them to one type (convert ints into strings for example). Rather, use pandas concat method instead, which means you turn each multidimensional array into its own dataframe. This method is only useful if the array's you're combining have the same data type.
numpy  column_stack  method  combine  arrays  python  python2.7  same  data  type  documentation  howto  example  guide  reference 
14 days ago by racl101
pandas.concat — pandas 0.22.0 documentation
Also, the best way I've found, so far, to merge multiple sets of columns of data with the same name number of rows but different number of columns of different data types while preserving those data types is two convert each set of rows into its own pandas dataframe and then to concatenate those dataframes by axis=1 (columns, use axis=0 for rows) in the order desired, into its own dataframe.
pandas  documentation  guide  reference  howto  concatenate  concat  method  dataframes  columns  preserving  data  type  python  python2.7 
15 days ago by racl101

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