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Python/ Pandas：找到左右最大值

[英]Python/ Pandas: Finding a left and right max

 原文  2020-09-13 23:31:38  5   1    python/ pandas/ loc

问题描述

I've got a pandas dataframe with an area in the first column and 8 years of quarterly data in the rest.我有一个 Pandas 数据框，第一列有一个区域，其余的有 8 年的季度数据。 There's about 4400 rows.大约有 4400 行。 Here is a sample:这是一个示例：

idx Q12000      Q22000      Q32000      Q42000      Q12001      Q22001      Q32001     Q42001      Q12002      Q22002      Q32002      Q42002

0   4085280.0   4114911.0   4108089.0   4111713.0   4055699.0   4076430.0   4043219.0  4039370.0   4201158.0   4243119.0   4231823.0   4254681.0
1   21226.0     21566.0     21804.0     22072.0     21924.0     23232.0     22748.0    22258.0     22614.0     22204.0     22500.0     22660.0     
2   96400.0     102000.0    98604.0     97086.0     96354.0     103054.0    97824.0    95958.0     115938.0    123064.0    120406.0    120648.0   
3   23820.0     24116.0     24186.0     23726.0     23504.0     23574.0     23162.0    23078.0     22306.0     22334.0     22152.0     22080.0     
4   7838.0      7906.0      7714.0      7676.0      7480.0      7520.0      7102.0     6722.0      8324.0      8166.0      8208.0      8326.0

Here is an image depicting what I'm trying to calculate: timeline这是一张描述我正在尝试计算的图像：时间线

  • nadir : the lowest point (min)最低点：最低点（分钟）
  • nadir_qtr : the quarter at which the nadir happens nadir_qtr : 最低点发生的季度
  • pre-peak : the highest point before the nadir pre-peak : 最低点前的最高点
  • pre-peak_qtr : the quarter at which the pre-peak happens pre-peak_qtr : 前峰值发生的季度
  • post-peak : the highest point after the nadir post-peak : 最低点之后的最高点
  • post-peak_qtr : the quarter at which the post-peak happens recover: the quarter after the nadir where the numbers surpass those of the pre-peak post-peak_qtr : 后峰值发生的季度恢复：最低点之后的季度，数字超过峰值前的数字

I'm able to calculate the nadir pretty easily.我可以很容易地计算出最低点。

df['nadir'] = df.iloc[:,2:].min(axis=1)
df['nadir_qtr'] = df.iloc[:,2:].idxmin(axis=1)

idx Q12000      Q22000      Q32000      Q42000      Q12001      Q22001      Q32001     Q42001      Q12002      Q22002      Q32002      Q42002      nadir      nadir_qtr

0   4085280.0   4114911.0   4108089.0   4111713.0   4055699.0   4076430.0   4043219.0  4039370.0   4201158.0   4243119.0   4231823.0   4254681.0   4039370.0  Q42001
1   21226.0     21566.0     21804.0     22072.0     21924.0     23232.0     22748.0    22258.0     22614.0     22204.0     22500.0     22660.0     21226      Q12000 
2   96400.0     102000.0    98604.0     97086.0     96354.0     103054.0    97824.0    95958.0     115938.0    123064.0    120406.0    120648.0    95958.0    Q42001  
3   23820.0     24116.0     24186.0     23726.0     23504.0     23574.0     23162.0    23078.0     22306.0     22334.0     22152.0     22080.0     22080.0    Q42002
4   7838.0      7906.0      7714.0      7676.0      7480.0      7520.0      7102.0     6722.0      8324.0      8166.0      8208.0      8326.0      6722.0     Q42001

But when it comes to getting the pre or post peak values or quarters, I get stuck hard.但是当谈到获得前或后峰值或季度时，我很难受困。 The closest I've come is something like this:我最接近的是这样的：

df['pre-peak'] = df.loc[:,:df['nadir_qtr'].max(axis=1)
df['pre-peak_qtr'] = df.loc[:,:df['nadir_qtr']].idxmax(axis=1)

Expected output:预期输出：

idx Q12000      Q22000      Q32000      Q42000      Q12001      Q22001      Q32001     Q42001      Q12002      Q22002      Q32002      Q42002      nadir      nadir_qtr   pre-peak      pre-peak_qtr

0   4085280.0   4114911.0   4108089.0   4111713.0   4055699.0   4076430.0   4043219.0  4039370.0   4201158.0   4243119.0   4231823.0   4254681.0   4039370.0  Q42001      4114911.0     Q22000
1   21226.0     21566.0     21804.0     22072.0     21924.0     23232.0     22748.0    22258.0     22614.0     22204.0     22500.0     22660.0     21226.0    Q12000      NaN           NaN
2   96400.0     102000.0    98604.0     97086.0     96354.0     103054.0    97824.0    95958.0     115938.0    123064.0    120406.0    120648.0    95958.0    Q42001      103054.0      Q22001
3   23820.0     24116.0     24186.0     23726.0     23504.0     23574.0     23162.0    23078.0     22306.0     22334.0     22152.0     22080.0     22080.0    Q42002      24816.0       Q32000
4   7838.0      7906.0      7714.0      7676.0      7480.0      7520.0      7102.0     6722.0      8324.0      8166.0      8208.0      8326.0      6722.0     Q42001      7906.0        Q2200

But any variations of this give me the wrong data or errors (the most common is)但是任何变化都会给我错误的数据或错误（最常见的是）

TypeError: reduction operation 'argmax' not allowed for this dtype类型错误：此 dtype 不允许缩减操作“argmax”

I've tried lots of strategies, brute-forcing iterating through each row as a numpy array, splitting each row.我尝试了很多策略，强制将每一行作为一个 numpy 数组进行迭代，分割每一行。 I'm really stuck.我真的被困住了。

1 个解决方案

解决方案1
 2 已采纳  2020-09-14 04:03:13

Here is one way, which uses 'helper' functions:这是一种使用“辅助”功能的方法：

# create the data frame
from io import StringIO
import pandas as pd

data = ''' Q12000      Q22000      Q32000      Q42000      Q12001      Q22001      Q32001     Q42001      Q12002      Q22002      Q32002      Q42002

0   4085280.0   4114911.0   4108089.0   4111713.0   4055699.0   4076430.0   4043219.0  4039370.0   4201158.0   4243119.0   4231823.0   4254681.0
1   21226.0     21566.0     21804.0     22072.0     21924.0     23232.0     22748.0    22258.0     22614.0     22204.0     22500.0     22660.0     
2   96400.0     102000.0    98604.0     97086.0     96354.0     103054.0    97824.0    95958.0     115938.0    123064.0    120406.0    120648.0   
3   23820.0     24116.0     24186.0     23726.0     23504.0     23574.0     23162.0    23078.0     22306.0     22334.0     22152.0     22080.0     
4   7838.0      7906.0      7714.0      7676.0      7480.0      7520.0      7102.0     6722.0      8324.0      8166.0      8208.0      8326.0      
'''
df = pd.read_csv(StringIO(data), sep='\s+', engine='python')

Second, define the helper functions:其次，定义辅助函数：

def calc_nadir(s):
    assert isinstance(s, pd.Series)
    return s.min()

def calc_nadir_qtr(s):
    return s.argmin()

def calc_pre_peak(s):
    return s[ : s.argmin()].max()

def calc_pre_peak_quarter(s):
    try:
        qtr = s[ : s.argmin()].argmax()
    except:
        qtr = None
    return qtr

def calc_post_peak(s):
    return s[s.argmin() : ].max()

def calc_post_peak_qtr(s):
    return s[s.argmin() : ].argmax() + s.argmin()

Third, we use the helper functions and assemble the results:第三，我们使用辅助函数并组合结果：

nadir = df.apply(lambda x: calc_nadir(x), axis=1).rename('nadir')
nadir_qtr = df.apply(lambda x: calc_nadir_qtr(x), axis=1).rename('nadir_qtr')

pre_peak = df.apply(lambda x: calc_pre_peak(x), axis=1).rename('pre_peak')
pre_peak_qtr = df.apply(lambda x: calc_pre_peak_quarter(x), axis=1).rename('pre_peak_qtr')

post_peak = df.apply(lambda x: calc_post_peak(x), axis=1).rename('post_peak')
post_peak_qtr = df.apply(lambda x: calc_post_peak_qtr(x), axis=1).rename('post_peak_qtr')

results = pd.concat([nadir, nadir_qtr, pre_peak, pre_peak_qtr, 
                     post_peak, post_peak_qtr], axis=1)
print(results)

       nadir  nadir_qtr   pre_peak  pre_peak_qtr  post_peak  post_peak_qtr
0  4039370.0          7  4114911.0           1.0  4254681.0             11
1    21226.0          0        NaN           NaN    23232.0              5
2    95958.0          7   103054.0           5.0   123064.0              9
3    22080.0         11    24186.0           2.0    22080.0             11
4     6722.0          7     7906.0           1.0     8326.0             11

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