从熊猫框架中的一系列数据中找出值和日期

Question

我正在用 python pandas 编写一个脚本，我必须在其中找到值和日期的第一个下降点，然后在哪里达到最大值，然后再下降值和日期。然后又是下降点值和日期。在下面显示的图表中，我标记了我想要从中获取日期和值的红色圆圈。我有一个脚本，但我需要提及获取值的日期，但我想提取日期和值，任何帮助将不胜感激。

代码：

import pandas as pd

df = pd.read_csv(r"D:\Data\2015_20.csv", parse_dates=["Date"])
df = df[["Date", "Mean"]]
df = df.set_index("Date")
z1 = df['2016-04-28' : '2017-02-22'].min()
z2 = df['2017-05-13' : '2018-02-02'].max()
z3 = df['2018-03-19' : '2019-03-04'].max() 
print("2016", '%.2f'%z1)
print("2017", '%.2f'%z2)
print("2018", '%.2f'%z3)

Answer 1

您可以使用argrelextrema 查找本地最小值和最大值：

from scipy.signal import argrelextrema

np.random.seed(0)
rs = np.random.randn(200)
xs = [0]
for r in rs:
    xs.append(xs[-1] * 0.9 + r)
df = pd.DataFrame(xs, columns=['data'], index=pd.date_range('2000-01-01',periods=len(xs)))

n = 5  # number of points to be checked before and after

# Find local peaks

df['min'] = df.iloc[argrelextrema(df.data.values, np.less_equal,
                    order=n)[0]]['data']
df['max'] = df.iloc[argrelextrema(df.data.values, np.greater_equal,
                    order=n)[0]]['data']

df['min_date'] = df.index.where(df['min'].notna())
df['max_date'] = df.index.where(df['max'].notna())

---

print (df.head(15))
                data       min       max   min_date   max_date
2000-01-01  0.000000  0.000000       NaN 2000-01-01        NaT
2000-01-02  1.764052       NaN       NaN        NaT        NaT
2000-01-03  1.987804       NaN       NaN        NaT        NaT
2000-01-04  2.767762       NaN       NaN        NaT        NaT
2000-01-05  4.731879       NaN       NaN        NaT        NaT
2000-01-06  6.126249       NaN  6.126249        NaT 2000-01-06
2000-01-07  4.536346       NaN       NaN        NaT        NaT
2000-01-08  5.032800       NaN       NaN        NaT        NaT
2000-01-09  4.378163       NaN       NaN        NaT        NaT
2000-01-10  3.837128       NaN       NaN        NaT        NaT
2000-01-11  3.864013       NaN       NaN        NaT        NaT
2000-01-12  3.621656  3.621656       NaN 2000-01-12        NaT
2000-01-13  4.713764       NaN       NaN        NaT        NaT
2000-01-14  5.003425       NaN       NaN        NaT        NaT
2000-01-15  4.624757       NaN       NaN        NaT        NaT

编辑：

来自真实数据的解决方案：

df['Date'] = pd.to_datetime(df['Date'])

df = df.set_index('Date')

from scipy.signal import argrelextrema
n = 5
s1 = df.iloc[argrelextrema(df.Mean.values, np.less_equal,
                          order=n)[0]]['Mean']
s2 = df.iloc[argrelextrema(df.Mean.values, np.greater_equal,
                          order=n)[0]]['Mean']

s = s1.append(s2).sort_index()
print (s)
Date
2016-05-18    0.293171
2016-11-04    0.692509
2017-05-13    0.232963
2017-09-10    0.675797
2017-11-09    0.528592
2018-04-03    0.189523
2018-11-09    0.713351
Name: Mean, dtype: float64

s.to_csv('out.csc')