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Wichtigen Libraries

Numpy

Array

import numpy as np

x = np.arange(10)
x+1
(x+1)**2
np.sin(x)
x > 3

2-D Arrays

table = np.arange(50).reshape(10,5)
table**2

Matrix

a = np.mat([[4, 3], [2, 1]])
b = np.mat([[1, 2], [3, 4]])
a*b

Matplotlib

Plot

import matplotlib.pyplot as plt
import numpy as np

t = np.linspace(0, 2*np.pi, 500)
plt.plot(t, np.sin(t))
plt.show()

Sankey

import matplotlib.pyplot as plt
from matplotlib.sankey import Sankey

s = Sankey()
s.add(flows=[0.7, 0.3,-0.5, -0.5],
  labels=['a', 'b', 'c', 'd'],
  orientations = [1, 1, -1, 0] )
s.finish()
plt.show()

Pandas

import pandas as pd
df = pd.read_csv('SchPark01.csv',
         sep=';',
         header = None,
         names = ['date', 'time', 'Gh', 'Ta'],
         parse_dates = [[0, 1]],
         skipinitialspace=True,
         index_col = 0
       )
df.Gh['2011-07-07 12:30']
df.Ta.mean()
df.plot()

Sympy

import sympy
from sympy.solvers import solve
x = sympy.Symbol('x')
solve(sympy.Eq(x**2, x+1), x)
sympy.expand(x*(x+1)*(x+3))

Optimize

from scipy.optimize import minimize
def f(x):
    return (x[0]+2)**2+(x[1]-3)**2

minimize(f, [0,0])

Uncertainties

# pip install uncertainties
from uncertainties import ufloat, umath
x = ufloat(39.5, 0.5)
x**2
umath.log(x)
y = x + 2
y
y - x

Many others