CODEWITHSUNDEEP
pythonpandasmatplotlibplot
Aumedi Wibisana
Aumedi Wibisana

Reputation: 11

Reversed graph plot

I was trying to plot a graph using Python's matplotlib. The data that I wanted to plot is this the data

I'm trying to plot y_i with respect to t (t the x axis and y_i the y axis), but somehow I ended up with this graph, graph image here

You can see that there is something wrong with it, and we all know it (as we go higher along the y axis, the number is getting lower instead of higher). The graph is supposed to have negative slope, how do I fix this? (All kind of help will be appreciated)

Here's my code:

import matplotlib.pyplot as plt
import pandas as pd

e = 2.71828183
k = -0.043594
C = 100
h = 5

def y(t):
    y = C*(e**(k*t))
    return y

def f(Y):
    f = k*Y
    return f

def y_i(Y, F):
    y_i = Y + h*F
    return y_i

#Y = y(x)    
#F = f(Y)
data_luruh = {}
data_luruh['t'] = [x for x in range(0, 101, 5)]
data_luruh['y'] = [y(x) for x in range(0, 101, 5)]
data_luruh['y_i'] = [0 for x in range(0, 101, 5)]
data_luruh['g'] = [0 for x in range(0, 101, 5)]

data_luruh['y_i'][0] = 'NA'
data_luruh['g'][0] = 'NA'

a = 1
while a <= 20:
    data_luruh['y_i'][a] = y_i(y((a-1)*5), f(y((a-1)*5)))
    data_luruh['g'][a] = data_luruh['y'][a] - data_luruh['y_i'][a]
    a += 1

df = pd.DataFrame(data=data_luruh)
print(df)

t_data = data_luruh['t']
y_data = data_luruh['y']
yi_data = data_luruh['y_i']

plt.xlabel('t')
plt.plot(data_luruh['y_i'], 'go')
plt.show()

Upvotes: 0

Views: 227

Answers (2)

Jondiedoop
Jondiedoop

Reputation: 3353

Don't use "NA" for missing values: it's a string and will lead to the other values being interpreted as a string as well. Instead, changing the lines to

import numpy as np
data_luruh['y_i'][0] = np.nan
data_luruh['g'][0] = np.nan

gives

graph with negative slope

Upvotes: 1

Peter
Peter

Reputation: 658

If you add NA as a string, the whole data column will be treated as an object (the numpy type for strings) column. Thus, the y-column will be sorted lexicographically.

Instead, use np.nan, which internally is treated like a number: 

import numpy as np

data_luruh['y_i'][0] = np.nan
data_luruh['g'][0] = np.nan

Upvotes: 1

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