Creating Pandas Dataframe between two Numpy arrays, then draw scatter plot

There are a number of ways to create DataFrames. Given 1-dimensional column vectors, you can create a DataFrame by passing it a dict whose keys are column names and whose values are the 1-dimensional column vectors:

import numpy as npimport pandas as pdx = np.random.randn(5)y = np.sin(x)df = pd.DataFrame({'x':x, 'y':y})df.plot('x', 'y', kind='scatter')

Complementing, you can use pandas Series, but the DataFrame must have been created.

import numpy as npimport pandas as pdx = np.linspace(0,2*np.pi)y = np.sin(x)#df = pd.DataFrame()#df['X'] = pd.Series(x)#df['Y'] = pd.Series(y)# You can MIXdf = pd.DataFrame({'X':x})df['Y'] = pd.Series(y) df.plot('X', 'Y', kind='scatter')

This is another way that might help

import numpy as npimport pandas as pdx = np.linspace(0,2*np.pi)y = np.sin(x)df = pd.DataFrame(data=np.column_stack((x,y)),columns=['X','Y'])

And also, I find the examples from karlijn (DatacCamp) very helpful

import numpy as npimport pandas as pdTAB = np.array([[''     ,'Col1','Col2'],                 ['Row1' ,   1  ,   2  ],                 ['Row2' ,   3  ,   4  ],                 ['Row3' ,   5 ,   6  ]])dados = TAB[1:,1:]linhas = TAB[1:,0]colunas = TAB[0,1:]DF = pd.DataFrame(    data=dados,    index=linhas,    columns=colunas)print('\nDataFrame:', DF)

In order to do what you want, I wouldn't use the DataFrame plotting methods. I'm also a former experimental physicist, and based on experience with ROOT I think that the Python analog you want is best accomplished using matplotlib. In matplotlib.pyplot there is a method, hist2d(), which will give you the kind of heat map you're looking for.

As for creating the dataframe, an easy way to do it is:

df=pd.DataFrame({'x':x, 'y':y})