在熊猫，多个列名中对数据框进行排序时出现问题

Question

I'm a beginner using python and pandas. I'm trying to save some results that I produced through a function. I'm having several problems at the moment to generate the dataframe with desired results. Here is an example of an iterable function that I wrote (To simplify the description I'm using the example of the area of several triangles. My real function is more complicated and has several intermediate steps):

Base = 5
H = [1, 2, 3, 4, 5]

for i in H:

    def Triangle_area():

        H = [i]
        ratio = (Base*H)
        area = np.divide(ratio,2)

        ms = pd.DataFrame(area, columns=[i])
        A = ms[i].mean()
        A1 = pd.DataFrame({'area':A}, index=[i])

        return A1


    areas = Triangle_area()
    print(areas)

The result is a dataframe or a series of dataframes as follows:

   area
1   0.5
   area
2   1.0
   area 
3   1.5
   area
4   2.0
   area
5   2.5

But what I want should look like this:

H   area
1   0.5
2   1.0
3   1.5
4   2.0
5   2.5

I think that there must exist several ways to do this, but I can not find the way to do it. Thanks in advance for your comments.

Answer 1

You're creating and returning a new dataframe each time in your example, which is not what you want. Here is an example of your triangle program that will return one dataframe with the results. I hope this helps and you can find how to apply it to your problem, let me know if you need more help

import pandas as pd
import numpy as np


def Triangle_area(height, base):
    '''
    Calculate the area of a right angle triangle, Area(height, base) = (base*height)/2
    Put all results in pandas dataframe before returning
    '''
    H = np.array(height)    # Make numpy array of heights, easier for our computations
    ratio = H * base        
    area = ratio/2
    A1 = pd.DataFrame({'H': height, 'area':area}) # Turn results into pandas dataframe

    return A1 # return column


Base = 5
H = [1, 2, 3, 4, 5]

areas = Triangle_area(H, Base)
print(areas)

Answer 2

Always try to use vectorized approach first:

In [115]: def Triangle_area(base, h):
     ...:     return base * h / 2.
     ...:

In [116]: df = pd.DataFrame({'base':[1,2,3,4,5], 'h':[5]*5})

In [117]: df
Out[117]:
   base  h
0     1  5
1     2  5
2     3  5
3     4  5
4     5  5

In [118]: df['area'] = Triangle_area(df['base'], df['h'])

In [119]: df
Out[119]:
   base  h  area
0     1  5   2.5
1     2  5   5.0
2     3  5   7.5
3     4  5  10.0
4     5  5  12.5

Answer 3

def Triangle_area():

    '''
    @param Base, scalar length of triangle base
    @param H, array of various triangle heights
    @returns A1, dataframe with columns 'H' and 'area'
        corresponding to height and area of triangle with 
        that height
    '''

    ratio = Base*np.array(H)
    area = np.divide(ratio,2)

    A1 = pd.DataFrame({'H':np.array(H),'area':area})

    return A1

areas = Triangle_area()
print(areas)

Here I tried to retain the same function and global variable names as you. A few tricks:

• Convert the list H into a NumPy array (NumPy works very well with pandas and I would recommend looking into it if you are not familiar with it already).

• Instead of using a for loop , operate on the arrays directly.