Question

请帮助我，我有问题。已经有两个星期了，但我还没明白。

因此，我想在数据帧中使用“应用”，这是我从Alphavantage API获得的。我想将欧几里德距离应用于数据帧的每一行。

import math
import numpy as np
import pandas as pd
from scipy.spatial import distance
import matplotlib.pyplot as plt
import matplotlib.patches as mpatches
from sklearn.neighbors import KNeighborsRegressor
from alpha_vantage.timeseries import TimeSeries
from services.KEY import getApiKey


ts = TimeSeries(key=getApiKey(), output_format='pandas')

在我的照片中我得到了

My chart (sorry can't post image because of my reputation)

在我的代码中

stock, meta_data = ts.get_daily_adjusted(symbol, outputsize='full')
stock = stock.sort_values('date')

open = stock['1. open'].values
low = stock['3. low'].values
high = stock['2. high'].values
close = stock['4. close'].values
sorted_date = stock.index.get_level_values(level='date')

stock_numpy_format = np.stack((sorted_date, open, low
                               ,high, close), axis=1)
df = pd.DataFrame(stock_numpy_format, columns=['date', 'open', 'low', 'high', 'close'])

df = df[df['open']>0]
df = df[(df['date'] >= "2016-01-01") & (df['date'] <= "2018-12-31")]
df = df.reset_index(drop=True)

df['close_next'] = df['close'].shift(-1)
df['daily_return'] = df['close'].pct_change(1)
df['daily_return'].fillna(0, inplace=True)
stock_numeric_close_dailyreturn = df['close', 'daily_return']
stock_normalized = (stock_numeric_close_dailyreturn - stock_numeric_close_dailyreturn.mean()) / stock_numeric_close_dailyreturn.std()

euclidean_distances = stock_normalized.apply(lambda row: distance.euclidean(row, date_normalized) , axis=1)
distance_frame = pd.DataFrame(data={"dist": euclidean_distances, "idx":euclidean_distances.index})
distance_frame.sort_values("dist", inplace=True)
second_smallest = distance_frame.iloc[1]["idx"]
most_similar_to_date = df.loc[int(second_smallest)]["date"]

我想要这样的图表

The chart that I want

还有这张图片中的代码

distance_columns = ['Close', 'DailyReturn']
stock_numeric = stock[distance_columns]
stock_normalized = (stock_numeric - stock_numeric.mean()) / stock_numeric.std()
stock_normalized.fillna(0, inplace = True)
date_normalized = stock_normalized[stock["Date"] == "2016-06-29"]
euclidean_distances = stock_normalized.apply(lambda row: distance.euclidean(row, date_normalized), axis = 1)
distance_frame = pandas.DataFrame(data = {"dist": euclidean_distances, "idx": euclidean_distances.index})
distance_frame.sort_values("dist", inplace=True)
second_smallest = distance_frame.iloc[1]["idx"]
most_similar_to_date = stock.loc[int(second_smallest)]["Date"]

我试图弄清楚，df.apply中来自熊猫格式和pandas.csv_reader中的“应用”是不同的。有没有其他选择可以以不同的格式（pandas和csv）具有相同的输出

谢谢！

nb：对不起，如果我的英语不好。

如何将欧几里德距离应用于数据框。计算每一行

0 个答案: