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when i try to run the code below i get this graph
my code:
from numpy import nan
import json
import os
import numpy as np
import subprocess
import math
import matplotlib.pyplot as plt
from statistics import mean, stdev
def smooth(t):
new_t = []
for i, x in enumerate(t):
neighbourhood = t[max(i-2,0): i+3]
m = mean(neighbourhood)
s = stdev(neighbourhood, xbar=m)
if abs(x - m) > s:
x = ( t[i - 1 + (i==0)*2] + t[i + 1 - (i+1==len(t))*2] ) / 2
new_t.append(x)
return new_t
def outLiersFN(*U):
outliers=[] # after preprocessing list
#preprocessing Fc =| 2*LF1 prev by 1 - LF2 prev by 2 |
c0 = -2 #(previous) by 2 #from original
c1 =-1 #(previous) #from original
c2 =0 #(current) #from original
c3 = 1 #(next) #from original
preP = U[0] # original list
if c2 == 0:
outliers.append(preP[0])
c1+=1
c2+=1
c0+=1
c3+=1
oldlen = len(preP)
M_RangeOfMotion = 90
while oldlen > c2 :
if c3 == oldlen:
outliers.insert(c2, preP[c2]) #preP[c2] >> last element in old list
break
if (preP[c2] > M_RangeOfMotion and preP[c2] < (preP[c1] + preP[c3])/2) or (preP[c2] < M_RangeOfMotion and preP[c2] > (preP[c1] + preP[c3])/2): #Check Paper 3.3.1
Equ = (preP[c1] + preP[c3])/2 #fn of preprocessing # From third index # ==== inserting current frame
formatted_float = "{:.2f}".format(Equ) #with .2 number only
equu = float(formatted_float) #from string float to float
outliers.insert(c2,equu) # insert the preprocessed value to the List
c1+=1
c2+=1
c0+=1
c3+=1
else :
Equ = preP[c2] # fn of preprocessing #put same element (do nothing)
formatted_float = "{:.2f}".format(Equ) # with .2 number only
equu = float(formatted_float) # from string float to float
outliers.insert(c2, equu) # insert the preprocessed value to the List
c1 += 1
c2 += 1
c0 += 1
c3 += 1
return outliers
def remove_nan(list):
newlist = [x for x in list if math.isnan(x) == False]
return newlist
the_angel = [176.04, 173.82, 170.09, 165.3, 171.8, 178.3, 178.77, 179.24, 179.93, 180.0, 173.39, 166.78, 166.03, 165.28, 165.72, 166.17, 166.71, 167.26, 168.04, 167.22, 166.68, 166.13, 161.53, 165.81, 170.1, 170.05, 170.5, 173.01, 176.02, 174.53, 160.09, 146.33, 146.38, 146.71, 150.33, 153.95, 154.32, 154.69, 134.52, 114.34, 115.6, 116.86, 134.99, 153.12, 152.28, 151.43, 151.36, 152.32, 158.9, 166.52, 177.74, 178.61, 179.47, 167.44, 155.4, 161.54, 167.68, 163.96, 160.24, 137.45, 114.66, 117.78, 120.89, 139.95, 139.62, 125.51, 111.79, 112.07, 112.74, 110.22, 107.7, 107.3, 106.52, 105.73, 103.07, 101.35, 102.5, 104.59, 104.6, 104.49, 104.38, 102.81, 101.25, 100.62, 100.25, 100.15, 100.32, 99.84, 99.36, 100.04, 100.31, 99.14, 98.3, 97.92, 97.41, 96.9, 96.39, 95.88, 95.9, 95.9, 96.02, 96.14, 96.39, 95.2, 94.56, 94.02, 93.88, 93.8, 93.77, 93.88, 94.04, 93.77, 93.65, 93.53, 94.2, 94.88, 92.59, 90.29, 27.01, 32.9, 38.78, 50.19, 61.59, 61.95, 62.31, 97.46, 97.38, 97.04, 96.46, 96.02, 96.1, 96.33, 95.61, 89.47, 89.34, 89.22, 89.48, 89.75, 90.02, 90.28, 88.16, 88.22, 88.29, 88.17, 88.17, 94.98, 94.84, 94.69, 94.94, 94.74, 94.54, 94.69, 94.71, 94.64, 94.58, 94.19, 94.52, 94.85, 87.7, 87.54, 87.38, 95.71, 96.57, 97.11, 97.05, 96.56, 96.07, 95.76, 95.56, 95.35, 95.28, 95.74, 96.2, 96.32, 96.33, 96.2, 96.14, 96.07, 96.07, 96.12, 96.17, 96.28, 96.31, 96.33, 96.16, 96.05, 95.94, 95.33, 88.96, 95.0, 95.78, 88.19, 88.19, 88.19, 87.92, 87.93, 88.03, 87.94, 87.86, 87.85, 87.89, 88.08, 88.01, 87.88, 88.02, 88.15, 88.15, 88.66, 88.73, 88.81, 88.41, 88.55, 88.68, 88.69, 88.02, 87.35, 95.19, 95.39, 95.38, 95.37, 95.27, 95.17, 95.33, 95.32, 95.31, 95.37, 95.42, 95.34, 95.44, 95.53, 95.47, 95.41, 95.13, 94.15, 94.78, 97.64, 97.1, 96.87, 97.03, 96.76, 35.44, 23.63, 23.27, 24.71, 26.16, 96.36, 113.13, 129.9, 96.82, 63.74, 34.25, 33.42, 32.6, 30.69, 31.06, 31.43, 97.14, 97.51, 97.23, 98.54, 100.13, 100.95, 28.82, 33.81, 66.81, 99.82, 102.63, 101.9, 101.44, 102.19, 103.22, 103.67, 104.13, 104.07, 104.73, 105.46, 103.74, 102.02, 103.32, 102.59, 29.54, 28.08, 28.76, 29.79, 30.82, 113.51, 129.34, 145.16, 143.18, 148.29, 153.67, 166.14, 161.16, 151.64, 149.27, 146.9, 151.67, 153.02, 149.28, 145.53, 149.1, 152.67, 158.78, 164.89, 164.84, 164.8, 162.11, 159.42, 156.73, 156.28, 155.83, 156.4, 161.0, 165.59, 164.44, 159.73, 155.76, 156.97, 158.92, 159.15, 159.39, 159.99, 160.44, 160.88, 163.89, 166.9, 167.71, 167.11, 167.0, 167.44, 168.38, 153.16, 137.94, 137.65, 152.09, 169.49, 171.36, 173.22, 174.01, 174.0, 174.2, 174.41, 157.74, 141.09, 149.32, 157.57, 156.4, 148.4, 140.78, 141.06, 141.73, 143.05, 143.91, 156.59, 169.29, 172.17, 175.05, 175.29, 175.27, 175.15, 175.02, 174.81, 174.59, 174.76, 174.94, 175.18, 175.41, 175.23, 174.51, 174.64, 174.77, 174.56, 173.25, 172.38, 174.17, 176.4, 177.27, 177.29, 177.33, 178.64, 179.98, 179.99, 176.0, 172.88, 173.77, 173.8, 173.97, 174.72, 175.24, 176.89, 179.07, 179.27, 178.78, 178.29, 175.61, 174.21, 172.8, 173.05, 173.41, 173.77, 174.65, 175.52, 175.58, 176.15, 176.71, 159.12, 141.54, 141.12, 155.62, 170.53, 165.54, 160.71, 158.22, 156.35, 156.82, 158.55, 160.27, 161.33, 162.39, 162.37, 159.48, 156.59, 156.77, 158.05, 159.32, 158.49, 157.66, 157.7, 157.74, 158.44, 159.14, 150.13, 143.06, 136.0, 125.7, 115.41, 111.19, 106.97, 107.1, 107.24, 107.45, 107.67, 113.34, 119.01, 144.87, 170.73, 174.31, 177.89, 174.78, 171.67, 163.26, 134.58, 105.9, 102.98, 100.77, 101.05, 101.39, 101.73, 99.79, 98.71, 97.64, 97.8, 97.89, 96.67, 95.45, 94.33, 93.38, 92.44, 48.53, 91.4, 91.35, 91.34, 91.33, 90.92, 90.51, 88.63, 87.0, 86.74, 86.48, 96.79, 96.09, 95.46, 95.39, 94.32, 93.25, 93.31, 93.37, 93.11, 92.57, 93.41, 94.25, 96.48, 92.71, 88.94, 90.07, 90.43, 78.06, 77.69, 77.32, 90.1, 89.15, 89.14, 88.85, 88.38, 87.63, 121.2, 120.66, 86.89, 86.42, 85.69, 84.86, 84.86, 85.34, 85.82, 86.07, 86.32, 85.82, 85.32, 86.23, 86.69, 87.15, 87.04, 86.87, 86.58, 86.0, 85.41, 85.41, 85.53, 85.66, 85.7, 85.72, 85.75, 85.92, 86.09, 85.77, 85.45, 84.94, 85.55, 86.16, 86.21, 86.1, 85.77, 85.27, 84.56, 84.99, 85.38, 85.42, 85.98, 86.54, 86.5, 86.45, 86.56, 86.63, 86.35, 86.08, 85.82, 85.51, 85.21, 84.6, 84.84, 84.97, 85.1, 86.12, 86.88, 86.8, 86.46, 86.47, 87.23, 87.8, 88.0, 88.08, 88.16, 87.72, 87.63, 87.37, 86.42, 86.48, 87.24, 87.97, 88.09, 88.19, 88.32, 88.44, 87.82, 87.2, 86.03, 85.78, 91.5, 93.0, 88.2, 88.52, 88.42, 87.28, 85.73, 85.62, 85.5, 85.5, 87.06, 87.6, 88.1, 88.31, 88.53, 88.77, 89.14, 89.52, 89.46, 89.4, 90.28, 89.74, 91.28, 92.17, 92.16, 92.15, 93.08, 94.0, 94.66, 95.32, 94.13, 93.7, 93.32, 93.69, 94.58, 95.47, 97.25, 99.03, 99.63, 99.67, 99.71, 100.33, 101.58, 103.36, 103.49, 103.41, 106.31, 109.34, 109.28, 109.21, 107.76, 106.31, 105.43, 104.94, 104.44, 111.19, 117.93, 115.59, 113.24, 116.15, 119.06, 125.43, 140.72, 156.0, 161.7, 143.52, 135.33, 127.13, 127.68, 148.68, 169.68, 172.2, 174.72, 174.75, 174.66, 158.57, 142.63, 145.13, 153.29, 161.45, 163.34, 165.24, 162.25, 159.89, 159.07, 156.39, 155.21, 156.04, 159.29, 160.07, 160.85, 163.45, 162.93, 161.71, 160.06, 158.4, 144.74, 132.64, 134.57, 150.22, 165.86, 172.95, 174.12, 175.3, 175.5, 176.31, 177.71, 179.72, 168.13, 156.55, 146.24, 155.75, 176.0, 175.99, 175.98, 176.0, 176.02, 176.25, 175.13, 174.26, 173.38, 173.37, 173.46, 176.34, 174.55, 172.77, 168.45, 166.35, 166.47, 168.81, 167.43, 166.79, 167.35, 168.65, 168.51, 168.37, 168.88, 169.74, 171.19, 171.33, 169.91, 168.49, 167.11, 166.83, 167.01, 168.68, 170.34, 170.43, 172.15, 173.86, 177.62, 177.61, 175.34, 173.06, 176.47, 179.87, 179.9, 177.67, 175.67, 175.39, 175.36, 177.03, 176.0, 174.98, 174.96, 174.94, 175.76, 176.57, 169.05, 162.99, 164.97, 168.74, 172.51, 167.38, 165.08, 163.03, 163.81, 164.83, 164.81, 164.8, 165.88, 165.36, 159.61, 153.86, 153.57, 153.61, 153.65, 154.62, 155.58, 157.97, 156.35, 155.66, 154.98, 156.11, 157.24, 159.25, 159.6, 160.43, 161.26, 164.71, 168.17, 147.46, 126.92, 106.38, 105.23, 104.4, 105.37, 106.65, 109.21, 107.44, 104.65, 101.86, 102.35, 102.84, 102.79, 102.19, 101.59, 100.98, 100.38, 98.72, 97.73, 97.32, 96.9, 95.11, 93.97, 94.12, 94.12, 93.1, 92.08, 89.29, 90.35, 90.35, 90.35, 90.35, 86.95, 86.37, 86.06, 85.74, 94.56, 93.16, 92.46, 91.76, 88.55, 85.33, 87.52, 92.18, 93.68, 95.18, 94.4, 92.17, 89.94, 89.4, 89.37, 99.44, 100.98, 102.52, 103.18, 88.96, 88.23, 87.5, 85.2, 85.19, 86.87, 121.42, 155.96, 155.97, 155.97, 86.2, 86.5, 86.8, 87.22, 87.36, 87.34, 87.03, 87.04, 87.05, 86.36, 85.68, 85.71, 85.84, 85.93, 86.01, 86.04, 86.08, 85.92, 86.05, 86.18, 86.17, 86.19, 86.23, 86.22, 86.09, 85.92, 85.66, 85.69, 85.69, 85.31, 84.91, 84.93, 84.95, 84.93, 84.91, 84.9, 84.9, 84.9, 84.9, 85.38, 85.52, 85.66, 85.66, 85.4, 85.14, 85.47, 85.8, 85.72, 85.64, 86.09, 85.84, 85.27, 85.47, 85.66, 85.59, 85.52, 85.38, 85.39, 85.28, 85.17, 85.39, 85.7, 85.98, 86.26, 86.61, 92.97, 93.15, 86.58, 86.58, 86.53, 86.47, 98.55, 99.41, 100.16, 100.9, 89.19, 90.28, 91.38, 91.39, 91.4, 91.44, 92.05, 131.05, 170.63, 170.13, 162.43, 125.64, 88.85, 88.85, 99.08, 100.38, 101.69, 100.74, 99.79, 96.33, 93.31, 93.73, 94.87, 96.01, 96.93, 97.85, 98.97, 97.85, 98.14, 99.37, 102.01, 103.8, 105.58, 108.52, 108.12, 107.72, 106.75, 106.82, 109.08, 112.37, 112.52, 112.66, 112.97, 114.12, 115.64, 117.1, 118.57, 126.13, 133.69, 149.27, 163.96, 166.62, 169.27, 164.94, 160.61, 149.35, 141.18, 143.41, 143.57, 149.26, 157.49, 159.94, 151.93, 147.47, 145.97, 145.56, 145.15, 143.85, 142.54, 142.18, 142.43, 143.12, 144.41, 144.38, 151.99, 159.59, 174.81, 174.94, 175.84, 176.87, 162.41, 152.94, 151.59, 155.24, 155.22, 155.19, 155.04]
p0 = outLiersFN(smooth(remove_nan(the_angel)))
the_angel = p0
plt.plot(the_angel) #list(filter(fun, L1))
plt.show()
print((the_angel))
how can i smooth the values in (the_angel) to get graph like this (red line)
i mean ignoring all unnecessary and noisy values and get only main line instead
you can edit my code or suggest me new filter or algorithm
pandas has a rolling() method for dataframes that you can use to calculate the mean over a window of values, e.g. the 70 closest ones:
import pandas as pd
import matplotlib.pyplot as plt
WINDOW_SIZE = 70
the_angel = [176.04, 173.82, 170.09, 165.3, 171.8, # ...
]
df = pd.DataFrame({'the angel': the_angel})
df[f'mean of {WINDOW_SIZE}'] = df['the angel'].rolling(
window=WINDOW_SIZE, center=True).mean()
df.plot(color=['blue', 'red']);
I have this code block:
def euc_dist(x,y):
return ((x[0] - y[0])**2 +(x[1] - y[1])**2 )**(1/2)
def dist(s1,s2):
distances = [euc_dist(s1[i],s2[i]) for i in range(s1.shape[0])]
return pd.Series(distances)
distances_df = tracking_data.loc[:,tracking_data[['away_player10_point', 'away_player9_point', 'away_player8_point', 'away_player7_point', 'away_player6_point', 'away_player5_point', 'away_player4_point', 'away_player3_point', 'away_player2_point', 'away_player1_point', 'away_player11_point', 'home_player1_point', 'home_player2_point', 'home_player3_point', 'home_player4_point', 'home_player5_point', 'home_player6_point', 'home_player7_point', 'home_player8_point', 'home_player9_point', 'home_player10_point', 'home_player11_point']].apply(tuple, axis = 1)].apply(dist, args = (tracking_data["ball_point"]))
tracking_data["closest"] = distances_df.idxmin(axis = 1).apply(lambda x: str(x)[:-6])
I am getting this error when running:
KeyError: "None of [Index([
((-22.06, -8.32), (-0.12, 21.38), (-1.49, -9.62), (-0.26, -28.52),
(-19.32, 16.22), (-15.11, 0.43), (-7.69, 32.87), (0.45, -0.25),
(-9.88, 7.67), (-47.29, -0.14), (-18.1, -25.42), (0.46, -19.84),
(7.58, 4.82), (15.33, -23.38), (21.08, 6.57), (14.98, 20.7), (8.14,
-4.27), (21.36, -9.06), (46.92, 0.01), (0.29, 9.88), (0.67, 22.24), (-0.06, -9.07)),\n
((-22.06, -8.32), (-0.07, 21.39), (-1.47, -9.64), (-0.23, -28.51),
(-19.31, 16.22), (-15.1, 0.42), (-7.68, 32.88), (0.46, -0.26), (-9.87,
7.7), (-47.3, -0.15), (-18.09, -25.41), (0.43, -19.83), (7.5600000000000005, 4.83), (15.31, -23.38), (21.06, 6.57), (14.97,
20.72), (8.12, -4.28), (21.33, -9.04), (46.91, 0.02), (0.25, 9.85), (0.67, 22.24), (-0.11, -9.05)),\n
((-22.06, -8.33), (-0.03, 21.39), (-1.43, -9.67), (-0.2, -28.5),
(-19.29, 16.24), (-15.09, 0.42), (-7.66, 32.9), (0.47000000000000003,
-0.27), (-9.85, 7.72), (-47.31, -0.16), (-18.08, -25.4), (0.39, -19.83), (7.55, 4.85), (15.28, -23.38), (21.03, 6.57), (14.95, 20.74), (8.09, -4.28), (21.28, -9.02), (46.91, 0.03), (0.2, 9.82), (0.66,
22.24), (-0.16, -9.02)),\n ((-22.06, -8.34), (0.01, 21.39), (-1.3900000000000001, -9.7), (-0.16, -28.5), (-19.28,
16.25), (-15.08, 0.42), (-7.64, 32.92), (0.49, -0.27), (-9.84, 7.75), (-47.32, -0.16), (-18.07, -25.4), (0.3500000000000...
Please reference this notebook to see my table as it is too large to put here. The work pertaining to this question is at the bottom.
https://github.com/piercepatrick/Articles_EDA/blob/main/nashSCProject.ipynb
I have been trying to work out this problem in my previous question: Pandas Dataframe: Find the column with the closest coordinate point to another columns coordinate point
I have a hunch that this issue lies in the source data since I originally loaded it in as JSON?
First, reset the index with
tracking_data = tracking_data.reset_index()
Then change
distances_df = tracking_data.loc[:,tracking_data[['away_player10_point', 'away_player9_point', 'away_player8_point', 'away_player7_point', 'away_player6_point', 'away_player5_point', 'away_player4_point', 'away_player3_point', 'away_player2_point', 'away_player1_point', 'away_player11_point', 'home_player1_point', 'home_player2_point', 'home_player3_point', 'home_player4_point', 'home_player5_point', 'home_player6_point', 'home_player7_point', 'home_player8_point', 'home_player9_point', 'home_player10_point', 'home_player11_point']].apply(tuple, axis = 1)].apply(dist, args = (tracking_data["ball_point"]))
for
distances_df = tracking_data[['away_player10_point', 'away_player9_point', 'away_player8_point', 'away_player7_point', 'away_player6_point', 'away_player5_point', 'away_player4_point', 'away_player3_point', 'away_player2_point', 'away_player1_point', 'away_player11_point', 'home_player1_point', 'home_player2_point', 'home_player3_point', 'home_player4_point', 'home_player5_point', 'home_player6_point', 'home_player7_point', 'home_player8_point', 'home_player9_point', 'home_player10_point', 'home_player11_point']].apply(dist, args = (tracking_data["ball_point"],))
I would like to calculate the maximum of 3 columns value.
import pandas as pd
import pandas_datareader.data as web
data = web.DataReader("^NSEI" , data_source="yahoo",start = "1/4/2016",end ="6/4/2018")
data=pd.DataFrame(data)
data["High-Low"] = data["High"] - data["Low"]
data["Close-low"] = abs(data["Close"].shift(1) - data["Low"])
data["Close-High"] = abs(data["Close"].shift(1) - data["High"])
data["True_Range"] = data[[data["High-Low"], data["Close-low"],data["Close-High"]]].max(axis=1)
In data["True _Range"] column, I want to take the maximum of value in data["High-low"],data["Close-low"] and data["Close-high"] columns. On this, it is giving a name error.
KeyError: "None of [Index([(156.44970703125, 67.9501953125, 79.75, 118.35009765625, 53.05029296875, 110.75, 100.5, 165.150390625, 161.0, 139.2001953125, 127.25, 98.60009765625, 229.39990234375, 148.7001953125, 105.7998046875, 65.94970703125, 58.19970703125, 59.25, 172.85009765625, 59.2001953125, 148.25, 69.10009765625, 91.099609375, 96.5, 149.349609375, 48.30029296875, 94.10009765625, 248.69970703125, 165.7998046875, 126.0, 166.94970703125, 163.05029296875, 87.25, 80.89990234375, 51.69970703125, 151.0, 81.0498046875, 72.80029296875, 67.7998046875, 268.80029296875, 200.39990234375, 72.2001953125, 77.900390625, 61.7998046875, 85.0, 114.7001953125, 99.7001953125, 83.35009765625, 68.650390625, 92.400390625, 102.85009765625, 105.89990234375, 95.7001953125, 95.849609375, 84.400390625, 56.25, 161.69970703125, 70.64990234375, 98.5, 75.60009765625, 74.0498046875, 60.05029296875, 147.64990234375, 46.89990234375, 94.89990234375, 42.64990234375, 161.94970703125, 54.0498046875, 92.599609375, 77.85009765625, 72.85009765625, 94.35009765625, 50.0, 84.0, 151.9501953125, 50.4501953125, 157.5498046875, 100.349609375, 52.5, 155.10009765625, 51.75, 70.69970703125, 60.5498046875, 120.10009765625, 59.19970703125, 112.2001953125, 66.39990234375, 96.7998046875, 101.75, 60.39990234375, 71.2998046875, 109.400390625, 76.64990234375, 98.39990234375, 45.75, 131.900390625, 134.5, 87.150390625, 49.2001953125, 79.2998046875, ...), (nan, 28.0498046875, 63.44970703125, 184.39990234375, 12.75, 107.0, 76.05029296875, 84.5, 118.60009765625, 109.5, 101.39990234375, 13.14990234375, 193.60009765625, 59.2998046875, 50.80029296875, 1.25, 16.44970703125, 28.14990234375, 21.85009765625, 22.2998046875, 127.900390625, 105.25, 4.150390625, 2.64990234375, 125.89990234375, 112.10009765625, 120.4501953125, 255.75, 107.35009765625, 75.849609375, 125.25, 87.60009765625, 19.39990234375, 45.7998046875, 10.0498046875, 143.849609375, 99.7998046875, 57.30029296875, 14.5, 203.9501953125, 48.05029296875, 85.85009765625, 37.19970703125, 31.5, 43.2001953125, 61.0, 84.39990234375, 25.5498046875, 4.849609375, 85.9501953125, 55.4501953125, 19.35009765625, 5.35009765625, 13.35009765625, 60.4501953125, 44.2998046875, 128.7998046875, 32.85009765625, 46.4501953125, 33.2001953125, 72.2998046875, 8.64990234375, 170.14990234375, 11.4501953125, 78.5, 19.75, 38.35009765625, 8.0498046875, 63.25, 7.7001953125, 37.150390625, 30.64990234375, 38.69970703125, 72.2998046875, 32.5, 22.10009765625, 145.44970703125, 58.5498046875, 72.5, 70.75, 49.75, 0.30029296875, 57.14990234375, 20.099609375, 28.349609375, 106.89990234375, 0.7998046875, 116.19970703125, 42.75, 18.9501953125, 80.0, 103.35009765625, 47.64990234375, 27.5, 15.25, 60.44970703125, 13.60009765625, 7.39990234375, 5.85009765625, 44.2001953125, ...)], dtype='object')] are in the [columns]"
Try:
data["True_Range"] = data[["High-Low","Close-low","Close-High"]].max(axis=1)
Instead of:
data["True_Range"] = data[[data["High-Low"], data["Close-low"],data["Close-High"]]].max(axis=1)
Your error signifies that you have input the data instead of the nested columnnames into your view of the dataframe, which produced the error.
Since the data from the columns themselves are not in the columnnames - you received the error.
One way of avoiding the error is:
data["True_Range"] = data[["High-Low", "Close-low", "Close-High"]].max(axis=1)
I am having a very weird and frustrating error.
The problem is very simple:
>>> mylist
[70.71, 67.23, 60.1, 62.52, 64.14, 65.4, 68.84, 61.04, 66.95, 62.22, 63.73, 62.04, 57.12, 61.3, 65.48, 61.49, 66.94, 62.68, 60.31, 64.38, 62.84, 63.03, 67.12, 60.65, 61.68, 64.0, 62.91, 61.36, 60.65, 62.45, 64.22, 66.4, 59.96, 57.03, 66.4, 60.43, 64.05, 64.09, 50.94, 39.84, 45.12, 63.39, 55.62, 55.58, 58.04, 59.91, 60.05, 57.3, 61.83, 63.87, 50.58, 62.56, 60.75, 58.9, 62.99, 61.65, 59.09, 59.91, 64.66, 61.1, 61.31, 59.62, 56.65, 60.1, 66.04, 60.57, 59.77, 53.0, 60.84, 61.75, 64.53, 52.0, 62.08, 67.69, 60.62, 55.04, 54.49, 30.82, 48.32, 59.66, 62.56, 66.71, 70.4, 55.13, 59.32, 63.06, 56.0, 56.19, 65.9, 61.01, 62.03, 59.43, 62.82, 56.83, 62.91, 76.44, 68.01, 61.0, 63.51, 58.12, 65.77, 56.88, 63.18, 69.03, 63.34, 54.91, 60.25, 64.79, 64.17, 61.86, 53.05, 63.42, 57.08, 60.36, 59.62, 64.69, 59.78, 58.34, 65.19, 55.49, 73.53, 56.25, 61.09, 53.35, 63.45, 64.34, 29.79, 59.75, 67.7, 59.74, 52.17, 61.29, 58.88, 55.68, 55.81, 57.52, 62.0, 62.49, 60.47, 61.86, 56.94, 56.31, 61.82, 62.01, 59.99, 59.4, 57.9, 63.08, 64.56, 60.77, 63.95, 60.1, 66.22, 58.05, 66.25, 62.34, 61.17, 64.04, 64.08, 61.1, 56.19, 61.04, 53.13, 64.58, 60.96, 58.97, 59.43, 66.22, 58.4, 55.68, 35.47, 42.6, 57.57, 58.3, 60.84, 62.52, 54.86, 60.9, 60.99, 60.47, 61.45, 58.34, 62.17, 63.68, 62.4, 64.62, 56.78, 59.14, 61.47, 54.7, 55.3, 58.09, 62.95, 61.47, 57.69, 62.45, 54.86, 58.51, 60.52, 54.56]
An now I try to get the min value:
>>> min(mylist)
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
TypeError: 'int' object is not callable
Why?
Most probably you have a variable named min that shadowed the built in min function. If you are using the interactive console just do:
del min
Also consider using numpy, as it can be faster on bigger lists:
>>> import numpy
>>> numpy.min(mylist)
I can not reproduce this. min(mylist) returns 29.79.
You are probably using a variable called min in your code. Don't do this.
I was trying to extract all the elements of the my data points (x,y) tuples, and put them into list of x values and y list, and transfer them to two columns in excel spreadsheet. It seems writing numbers into file is quite difficult. Can anyone shed a light on this problem?
Current state:
xlist=[list[i][0] for i in range(len(list))]
ylist=[list[i][1] for i in range(len(list))]
fob=open('c:/test/a.txt','w')
fob.write(xlist[i] for i in range(len(xlist))
i want to write down a column of numbers in notepad so that I can highlight and copy into spread sheet directly .
Below are my data.
list = [(0.496, 12.49), (0.531, 12.40), (0.578, 12.18), (0.615,
11.96), (0.657, 11.75), (0.731, 11.28), (0.785, 10.85), (0.812,
10.61), (0.883, 9.92), (0.930, 9.40), (0.979, 8.77), (1.026,
8.10), (1.081, 7.23), (1.134, 6.33), (1.189, 5.39), (1.220,
4.85), (1.273, 3.92), (1.332, 2.91), (1.364, 2.55), (1.418,
2.16), (1.467, 1.65), (1.523, 1.17), (1.569, 0.82), (1.626,
0.47), (1.678, 0.21), (1.723, 0.01), (1.776, 0.19), (1.814,
0.28), (1.869, 0.36), (1.933, 0.36), (1.972, 0.31), (2.021,
0.18), (2.081, 0.13), (2.129, 0.46), (2.169, 0.79), (2.219,
1.24), (2.280, 1.84), (2.306, 2.11), (2.358, 2.67), (2.414,
3.37), (2.471, 4.05), (2.505, 4.51), (2.562, 5.22), (2.613,
5.84), (2.652, 6.31), (2.712, 7.01), (2.758, 7.52), (2.802,
7.99), (2.869, 8.63), (2.930, 9.16), (2.971, 9.57), (3.043,
10.35), (3.078, 10.69), (3.119, 11.00), (3.174, 11.26), (3.217,
11.40), (3.261, 11.53), (3.307, 11.55), (3.371, 11.51), (3.432,
11.40), (3.479, 11.26), (3.507, 11.20), (3.557, 11.00), (3.623,
10.55), (3.663, 10.28), (3.729, 9.79), (3.768, 9.57), (3.825,
9.24), (3.880, 8.85), (3.944, 8.41), (3.969, 8.04), (4.014,
7.55), (4.086, 6.67), (4.105, 6.37), (4.166, 5.50), (4.212,
4.88), (4.266, 4.20), (4.311, 3.69), (4.364, 3.06), (4.401,
2.65), (4.453, 2.09), (4.497, 1.68), (4.556, 1.18), (4.602,
0.85), (4.644, 0.57), (4.695, 0.29), (4.754, 0.04), (4.799,
0.11), (4.847, 0.17), (4.918, 0.11), (4.959, 0.04), (4.992,
0.19), (5.063, 0.64), (5.098, 0.90), (5.157, 1.40), (5.201,
1.79), (5.245, 2.20), (5.291, 2.65), (5.326, 3.00), (5.387,
3.65), (5.420, 4.02), (5.469, 4.62), (5.538, 5.44), (5.579,
5.96), (5.629, 6.57), (5.674, 7.14), (5.724, 7.73), (5.798,
8.60), (5.823, 8.88), (5.888, 9.62), (5.919, 9.94), (5.963,
10.41), (6.009, 10.85), (6.050, 11.22), (6.115, 11.71), (6.153,
11.99), (6.222, 12.39), (6.263, 12.61), (6.302, 12.77), (6.377,
12.99), (6.414, 13.03), (6.454, 13.02), (6.522, 12.89), (6.558,
12.74), (6.626, 12.41), (6.677, 12.05), (6.729, 11.64), (6.791,
11.00), (6.832, 10.58), (6.887, 9.92), (6.949, 9.13), (6.996,
8.48), (7.028, 8.09), (7.094, 7.13), (7.123, 6.70), (7.161,
6.16), (7.213, 5.35), (7.250, 4.81), (7.332, 3.61), (7.382,
2.93), (7.420, 2.45), (7.474, 1.88), (7.514, 1.40), (7.576,
0.71), (7.600, 0.50), (7.662, 0.12), (7.725, 0.16), (7.768,
0.26), (7.810, 0.30), (7.858, 0.26), (7.904, 0.18), (7.980,
0.10), (8.021, 0.29), (8.078, 0.65), (8.133, 1.06), (8.165,
1.33), (8.218, 1.83), (8.267, 2.31), (8.321, 2.87), (8.355,
3.27), (8.413, 3.91), (8.473, 4.61), (8.519, 5.22), (8.553,
5.65), (8.643, 6.74), (8.678, 7.23), (8.734, 7.94), (8.760,
8.27), (8.803, 8.81), (8.851, 9.35), (8.905, 9.94), (8.961,
10.45), (9.009, 10.92), (9.053, 11.34), (9.106, 11.75), (9.166,
12.14), (9.228, 12.48), (9.292, 12.71), (9.340, 12.86), (9.384,
13.01), (9.412, 13.05), (9.452, 13.03), (9.472, 13.00)]
Cheers
Export it into a CSV file. Your use case is very simple and you should be able to do it using standard Python.
with open('output.csv', 'w') as f:
for x, y in l:
f.write("%s, %s\n" % (x, y))
Note: list is a reserved word in python and you should not be using it.
Use openpyxl to write .xslx files from Python:
import openpyxl
my_list = [(0.496, 12.49), (0.531, 12.40), (0.578, 12.18), (0.615,
11.96), (0.657, 11.75), (0.731, 11.28), (0.785, 10.85), (0.812,
10.61), (0.883, 9.92), (0.930, 9.40), (0.979, 8.77), (1.026,
8.10), (1.081, 7.23), (1.134, 6.33), (1.189, 5.39), (1.220,
4.85), (1.273, 3.92), (1.332, 2.91), (1.364, 2.55), (1.418,
2.16), (1.467, 1.65), (1.523, 1.17), (1.569, 0.82), (1.626,
0.47), (1.678, 0.21), (1.723, 0.01), (1.776, 0.19), (1.814,
0.28), (1.869, 0.36), (1.933, 0.36), (1.972, 0.31), (2.021,
0.18), (2.081, 0.13), (2.129, 0.46), (2.169, 0.79), (2.219,
1.24), (2.280, 1.84), (2.306, 2.11), (2.358, 2.67), (2.414,
3.37), (2.471, 4.05), (2.505, 4.51), (2.562, 5.22), (2.613,
5.84), (2.652, 6.31), (2.712, 7.01), (2.758, 7.52), (2.802,
7.99), (2.869, 8.63), (2.930, 9.16), (2.971, 9.57), (3.043,
10.35), (3.078, 10.69), (3.119, 11.00), (3.174, 11.26), (3.217,
11.40), (3.261, 11.53), (3.307, 11.55), (3.371, 11.51), (3.432,
11.40), (3.479, 11.26), (3.507, 11.20), (3.557, 11.00), (3.623,
10.55), (3.663, 10.28), (3.729, 9.79), (3.768, 9.57), (3.825,
9.24), (3.880, 8.85), (3.944, 8.41), (3.969, 8.04), (4.014,
7.55), (4.086, 6.67), (4.105, 6.37), (4.166, 5.50), (4.212,
4.88), (4.266, 4.20), (4.311, 3.69), (4.364, 3.06), (4.401,
2.65), (4.453, 2.09), (4.497, 1.68), (4.556, 1.18), (4.602,
0.85), (4.644, 0.57), (4.695, 0.29), (4.754, 0.04), (4.799,
0.11), (4.847, 0.17), (4.918, 0.11), (4.959, 0.04), (4.992,
0.19), (5.063, 0.64), (5.098, 0.90), (5.157, 1.40), (5.201,
1.79), (5.245, 2.20), (5.291, 2.65), (5.326, 3.00), (5.387,
3.65), (5.420, 4.02), (5.469, 4.62), (5.538, 5.44), (5.579,
5.96), (5.629, 6.57), (5.674, 7.14), (5.724, 7.73), (5.798,
8.60), (5.823, 8.88), (5.888, 9.62), (5.919, 9.94), (5.963,
10.41), (6.009, 10.85), (6.050, 11.22), (6.115, 11.71), (6.153,
11.99), (6.222, 12.39), (6.263, 12.61), (6.302, 12.77), (6.377,
12.99), (6.414, 13.03), (6.454, 13.02), (6.522, 12.89), (6.558,
12.74), (6.626, 12.41), (6.677, 12.05), (6.729, 11.64), (6.791,
11.00), (6.832, 10.58), (6.887, 9.92), (6.949, 9.13), (6.996,
8.48), (7.028, 8.09), (7.094, 7.13), (7.123, 6.70), (7.161,
6.16), (7.213, 5.35), (7.250, 4.81), (7.332, 3.61), (7.382,
2.93), (7.420, 2.45), (7.474, 1.88), (7.514, 1.40), (7.576,
0.71), (7.600, 0.50), (7.662, 0.12), (7.725, 0.16), (7.768,
0.26), (7.810, 0.30), (7.858, 0.26), (7.904, 0.18), (7.980,
0.10), (8.021, 0.29), (8.078, 0.65), (8.133, 1.06), (8.165,
1.33), (8.218, 1.83), (8.267, 2.31), (8.321, 2.87), (8.355,
3.27), (8.413, 3.91), (8.473, 4.61), (8.519, 5.22), (8.553,
5.65), (8.643, 6.74), (8.678, 7.23), (8.734, 7.94), (8.760,
8.27), (8.803, 8.81), (8.851, 9.35), (8.905, 9.94), (8.961,
10.45), (9.009, 10.92), (9.053, 11.34), (9.106, 11.75), (9.166,
12.14), (9.228, 12.48), (9.292, 12.71), (9.340, 12.86), (9.384,
13.01), (9.412, 13.05), (9.452, 13.03), (9.472, 13.00)]
book = openpyxl.Workbook()
sheet = book.active
for i, value in enumerate(my_list):
sheet.cell(row=i+1, column=1).value = value[0]
sheet.cell(row=i+1, column=2).value = value[1]
book.save('test.xlsx')
When you have data like numbers or objects in memory, it's generally not correct to dump that data directly into disk, you'll want to serialize it.
The easiest way to serialize it is with print which automatically calls the "serialization" method __str__. The problem with this serialization method is that's not always easy to deserialize.
When you have a data structure, like the matrix you describe, you'll want a serialization method that will preserve the structure and allow to reconstruct it in memory. In this case you can use CSV (through the csv module), JSON (through the json module) or many others.
Use CSV.