重复行x基于linspace的次数：np.linspace（RFMIn，RFMax，10）

Question

我使用linspace计算出了RFMin和RFMax之间的元素序列，但现在我需要一个符合RFRange=np.linspace（RFMin，RFMax，10）的表示法。我该怎么做

符号、RFMin和RFMax

A100K 1000 3333
B220K 5125.5 5888

import pandas as pd
Import numpy as np

df = csv.read_csv(filePath)
Notation = df['Notation']
RFRange = np.linspace(RFMIn, RFMax, 10)
RFRange = RFRange.transpose().flatten()
RFarray=[]
for i in RFRange:
       RFarray.append(i)
dict = {‘RFRange’: RFarray}
data = pd.DataFrame(dict)
data.to_csv(‘Output.csv’, header=True, sep=’\t’)

**我想要这样的东西：

A100K 1000
A100K 1259.22
A100K 1518.44
A100K 1777.67
……..
…….
A100K 3333
B220K 5125.5
B220K 5210.22
B220K 5294.94
……..
…….
B220K 5888

Answer 1

一个简单的解决方案是扩展Notation和RFrange

试试代码

#! /usr/bin/env python3

import pandas as pd
import numpy as np

df = pd.read_csv(filePath)
RFRange = np.zeros((df.shape[0],10))
for ndx, row in df.iterrows():
    RFRange[ndx] = np.linspace(row['RFMin'], row['RFMax'], 10)
RFarray=[]
Notation=[]
for ir, r1 in enumerate(RFRange):
    for i in r1:
       RFarray.append(i)
       Notation.append(df.loc[ir, 'Notation'])
dict = {'Notation': Notation, 'RFRange': RFarray}
data = pd.DataFrame(dict)
data.to_csv('Output.csv', header=True, sep='\t',
             index=False, float_format='%.2f')