Table of Contents
a = 2 # integer
b = 5.0 # float
c = 8.3e5 # exponential
d = 1.5 + 0.5j # complex
e = 4 > 5 # boolean
f = 'word' # stringa = ['red', 'blue', 'green'] # manually initialization
b = list(range(5)) # initialize from iteratable
c = [nu**2 for nu in b] # list comprehension
d = [nu**2 for nu in b if nu < 3] # conditioned list comprehension
e = c[0] # access element
f = c[1:2] # access a slice of the list
g = ['re', 'bl'] + ['gr'] # list concatenation
h = ['re'] * 5 # repeat a list
['re', 'bl'].index('re') # returns index of 're'
're' in ['re', 'bl'] # true if 're' in list
sorted([3, 2, 1]) # returns sorted lista = {'red': 'rouge', 'blue': 'bleu'} # dictionary
b = a['red'] # translate item
c = [value for key, value in a.items()] # loop through contents
d = a.get('yellow', 'no translation found') # return default
a.setdefault('extra', []).append('cyan') # init key with defaulta = 'red' # assignment
char = a[2] # access individual characters
'red ' + 'blue' # string concatenation
'1, 2, three'.split(',') # split string into list
'.'.join(['1', '2', 'three']) # concatenate list into stringa = 2 # assignment
a += 1 (*=, /=) # change and assign
3 + 2 # addition
3 / 2 # integer (python2) or float (python3) division
3 // 2 # integer division
3 * 2 # multiplication
3 ** 2 # exponent
3 % 2 # remainder
abs(a) # absolute value
1 == 1 # equal
2 > 1 # larger
2 < 1 # smaller
1 != 2 # not equal
1 != 2 and 2 < 3 # logical AND
1 != 2 or 2 < 3 # logical OR
not 1 == 2 # logical NOT
'a' in b # test if a is in b
a is b # test if objects point to the same memory (id)# if/elif/else
a, b = 1, 2
if a + b == 3:
print('True')
elif a + b == 1:
print('False')
else:
print('?')
# for
a = ['red', 'blue', 'green']
for color in a:
print(color)
# while
number = 1
while number < 10:
print(number)
number += 1
# break
number = 1
while True:
print(number)
number += 1
if number > 10:
break
# continue
for i in range(20):
if i % 2 == 0:
continue
print(i)# Function groups code statements and possibly
# returns a derived value
def myfunc(a1, a2):
return a1 + a2
x = myfunc(a1, a2)
# Class groups attributes (data)
# and associated methods (functions)
class Point(object):
def __init__(self, x):
self.x = x
def __call__(self):
print(self.x)
x = Point(3)
# Generator iterates without
# creating all values at ones
def firstn(n):
num = 0
while num < n:
yield num
num += 1
x = [i for i in firstn(10)]
# Decorator can be used to modify
# the behaviour of a function
class myDecorator(object):
def __init__(self, f):
self.f = f
def __call__(self):
print("call")
self.f()
@myDecorator
def my_funct():
print('func')
my_funct()<object>? # Information about the object
<object>.<TAB> # tab completion
# measure runtime of a function:
%timeit range(1000)
100000 loops, best of 3: 7.76 us per loop
# run scripts and debug
%run
%run -d # run in debug mode
%run -t # measures execution time
%run -p # runs a profiler
%debug # jumps to the debugger after an exception
%pdb # run debugger automatically on exception
# examine history
%history
%history ~1/1-5 # lines 1-5 of last session
# run shell commands
!make # prefix command with "!"
# clean namespace
%resetn # execute next line
b 42 # set breakpoint in the main file at line 42
b myfile.py:42 # set breakpoint in 'myfile.py' at line 42
c # continue execution
l # show current position in the code
p data # print the 'data' variable
pp data # pretty print the 'data' variable
s # step into subroutine
a # print arguments that a function received
pp locals() # show all variables in local scope
pp globals() # show all variables in global scopeipython --pdb -- myscript.py argument1 --option1 # debug after exception
ipython -i -- myscript.py argument1 --option1 # console after finishnp.array([2, 3, 4]) # direct initialization
np.empty(20, dtype=np.float32) # single precision array of size 20
np.zeros(200) # initialize 200 zeros
np.ones((3,3), dtype=np.int32) # 3 x 3 integer matrix with ones
np.eye(200) # ones on the diagonal
np.zeros_like(a) # array with zeros and the shape of a
np.linspace(0., 10., 100) # 100 points from 0 to 10
np.arange(0, 100, 2) # points from 0 to <100 with step 2
np.logspace(-5, 2, 100) # 100 log-spaced from 1e-5 -> 1e2
np.copy(a) # copy array to new memorya = np.arange(100) # initialization with 0 - 99
a[:3] = 0 # set the first three indices to zero
a[2:5] = 1 # set indices 2-4 to 1
a[start:stop:step] # general form of indexing/slicing
a[None, :] # transform to column vector
a[[1, 1, 3, 8]] # return array with values of the indices
a = a.reshape(10, 10) # transform to 10 x 10 matrix
a.T # return transposed view
b = np.transpose(a, (1, 0)) # transpose array to new axis order
a[a < 2] # values with elementwise conditiona.shape # a tuple with the lengths of each axis
len(a) # length of axis 0
a.ndim # number of dimensions (axes)
a.sort(axis=1) # sort array along axis
a.flatten() # collapse array to one dimension
a.conj() # return complex conjugate
a.astype(np.int16) # cast to integer
np.argmax(a, axis=1) # return index of maximum along a given axis
np.cumsum(a) # return cumulative sum
np.any(a) # True if any element is True
np.all(a) # True if all elements are True
np.argsort(a, axis=1) # return sorted index array along axisa < 2 # returns array with boolean values
(a < 2) & (b > 10) # elementwise logical and
(a < 2) | (b > 10) # elementwise logical or
~a # invert boolean arraya * 5 # multiplication with scalar
a + 5 # addition with scalar
a + b # addition with array b
a / b # division with b (np.NaN for division by zero)
np.exp(a) # exponential (complex and real)
np.power(a, b) # a to the power b
np.sin(a) # sine
np.cos(a) # cosine
np.arctan2(a, b) # arctan(a/b)
np.arcsin(a) # arcsin
np.radians(a) # degrees to radians
np.degrees(a) # radians to degrees
np.var(a) # variance of array
np.std(a, axis=1) # standard deviationnp.dot(a, b) # inner product: a_mi b_in
np.einsum('ij,kj->ik', a, b) # einstein summation convention
np.sum(a, axis=1) # sum over axis 1
np.abs(a) # return absolute values
a[None, :] + b[:, None] # outer sum
a[None, :] * b[:, None] # outer product
np.outer(a, b) # outer product
np.sum(a * a.T) # matrix normnp.fromfile(fname/fobject, dtype=np.float32, count=5) # binary data from file
np.loadtxt(fname/fobject, skiprows=2, delimiter=',') # ascii data from file
np.savetxt(fname/fobject, array, fmt='%.5f') # write ascii data
np.tofile(fname/fobject) # write (C) binary datanp.trapz(a, x=x, axis=1) # integrate along axis 1
np.interp(x, xp, yp) # interpolate function xp, yp at points x
np.linalg.lstsq(a, b) # solve a x = b in least square sensenp.fft.fft(a) # complex fourier transform of a
f = np.fft.fftfreq(len(a)) # fft frequencies
np.fft.fftshift(f) # shifts zero frequency to the middle
np.fft.rfft(a) # real fourier transform of a
np.fft.rfftfreq(len(a)) # real fft frequenciesnp.ceil(a) # rounds to nearest upper int
np.floor(a) # rounds to nearest lower int
np.round(a) # rounds to neares intfrom np.random import normal, seed, rand, uniform, randint
normal(loc=0, scale=2, size=100) # 100 normal distributed
seed(23032) # resets the seed value
rand(200) # 200 random numbers in [0, 1)
uniform(1, 30, 200) # 200 random numbers in [1, 30)
randint(1, 16, 300) # 300 random integers in [1, 16)fig = plt.figure(figsize=(5, 2)) # initialize figure
ax = fig.add_subplot(3, 2, 2) # add second subplot in a 3 x 2 grid
fig, axes = plt.subplots(5, 2, figsize=(5, 5)) # fig and 5 x 2 nparray of axes
ax = fig.add_axes([left, bottom, width, height]) # add custom axisfig.suptitle('title') # big figure title
fig.subplots_adjust(bottom=0.1, right=0.8, top=0.9, wspace=0.2,
hspace=0.5) # adjust subplot positions
fig.tight_layout(pad=0.1, h_pad=0.5, w_pad=0.5,
rect=None) # adjust subplots to fit into fig
ax.set_xlabel('xbla') # set xlabel
ax.set_ylabel('ybla') # set ylabel
ax.set_xlim(1, 2) # sets x limits
ax.set_ylim(3, 4) # sets y limits
ax.set_title('blabla') # sets the axis title
ax.set(xlabel='bla') # set multiple parameters at once
ax.legend(loc='upper center') # activate legend
ax.grid(True, which='both') # activate grid
bbox = ax.get_position() # returns the axes bounding box
bbox.x0 + bbox.width # bounding box parametersax.plot(x,y, '-o', c='red', lw=2, label='bla') # plots a line
ax.scatter(x,y, s=20, c=color) # scatter plot
ax.pcolormesh(xx, yy, zz, shading='gouraud') # fast colormesh
ax.colormesh(xx, yy, zz, norm=norm) # slower colormesh
ax.contour(xx, yy, zz, cmap='jet') # contour lines
ax.contourf(xx, yy, zz, vmin=2, vmax=4) # filled contours
n, bins, patch = ax.hist(x, 50) # histogram
ax.imshow(matrix, origin='lower',
extent=(x1, x2, y1, y2)) # show image
ax.specgram(y, FS=0.1, noverlap=128,
scale='linear') # plot a spectrogram
ax.text(x, y, string, fontsize=12, color='m') # write textfrom scipy.ndimage import map_coordinates # interpolates data
pts_new = map_coordinates(data, float_indices, # at index positions
order=3)from scipy.integrate import quad # definite integral of python
value = quad(func, low_lim, up_lim) # function/methods = pd.Series(np.random.rand(1000), index=range(1000)) # series
index = pd.date_range("13/06/2016", periods=1000) # time index
df = pd.DataFrame(np.zeros((1000, 3)), index=index,
columns=["A", "B", "C"]) # DataFramedf = pd.read_csv("filename.csv") # read and load CSV file in a DataFrame
print(df[:2]) # print first 2 lines of the DataFrame
raw = df.values # get raw data out of DataFrame object
cols = df.columns # get list of columns headers