在 Pandas 中寻找增长趋势
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Finding increasing trend in Pandas
提问by Aristotle Tan Yi Sing
Given a set of (time-series) data, how to interpret the data in such a way that it is increasing/decreasing, not steady, unchanged, etc.
给定一组（时间序列）数据，如何以增加/减少、不稳定、不变等方式解释数据。
Year Revenue
1993 0.85
1994 0.99
1995 1.01
1996 1.12
1997 1.25
1998 1.36
1999 1.28
2000 1.44
回答by Ashish
you can use numpy.polyfit, you can provide order as Degree of the fitting polynomial.
您可以使用 numpy.polyfit，您可以提供阶数作为拟合多项式的度数。
Refer:numpy.polyfit documentation
import numpy as np
import pandas as pd
def trendline(data, order=1):
coeffs = np.polyfit(data.index.values, list(data), order)
slope = coeffs[-2]
return float(slope)
#Sample Dataframe
revenue = [0.85, 0.99, 1.01, 1.12, 1.25, 1.36, 1.28, 1.44]
year = [1993, 1994, 1995, 1996, 1997, 1998, 1999, 2000]
df = pd.DataFrame({'year': year, 'revenue': revenue})
slope = trendline(df['revenue'])
print slope
so now if the value of slope is +ve the trend is increasing, if it is 0 trend is constant, else decreasing
所以现在如果斜率的值是 +ve 趋势正在增加，如果它是 0 趋势是恒定的，否则减少
In your given data slope is 0.0804761904762. So, the trend is increasing
在您给定的数据斜率为 0.0804761904762。所以趋势是越来越大
回答by piRSquared
if you sort the dataframe by 'Year'
如果您对数据框进行排序 'Year'
df.sort_values('Year', inplace=True)
You can then observe the pd.Seriesattributesdf.Revenue.is_monotonicdf.Revenue.is_monotonic_decreasingdf.Revenue.is_monotonic_increasing
然后你可以观察pd.Series属性df.Revenue.is_monotonicdf.Revenue.is_monotonic_decreasingdf.Revenue.is_monotonic_increasing
