pandas dataframe：通过浮点范围的变量选择行

Question

I am new ty Python and wanted to find a solution to select rows by a variable which should be a range of floating points. As a variable I assign numpy array made with numpy arange since python's built-in range does not take float as step. Here's an example of what I tried:

import numpy as np
import pandas as pd
data = [[1, 100], [2,200], [3, 300], [4, 400], [5, 500], 
    [5.5, 550], [6, 600], [6.5, 620]] 
df = pd.DataFrame(data, columns = ['small_number', 'big number'])

rng = np.arange(2, 7, 0.1)
df.loc[df.small_number.isin(rng)]

Unfortunately, the gives only the first value and does not find other. The output:

small_number    big number
1   2.0         200

How could I make a float range variable which then will be passed to select DataFrame rows?

Answer 1

You're going to need to round in order to check for existence in the array. The float numbers in the array are not precise as stated in the documentation .

When using a non-integer step, such as 0.1, the results will often not be consistent.

In this case we can use np.around and round to one decimal place.

df.loc[df.small_number.isin(np.around(rng, decimals=1))] 

   small_number  big number
1           2.0         200
2           3.0         300
3           4.0         400
4           5.0         500
5           5.5         550
6           6.0         600
7           6.5         620