#general imports
import pandas as pd
import numpy as np
import matplotlib as mpl
mpl.use('nbAgg')#for using it as inline in jupyter notebook
import matplotlib.pyplot as plt
import seaborn as sns
import statsmodels as sm
#if we want to get file path
print(plt.__file__)#here we want to get the path of plt function
############################ matplotlib ###########################
#** some (very few) of the below codes might not work, or you might have to make some changes based on the installation and version you are using
#to get matplotlib version
matplotlib.__version__
#making a sine plot, line plot
def sinplot(flip=1):
x = np.linspace(0,14,100)
for i in range(1, 7):
plt.plot(x, np.sin(x + i * .5) * (7 - i) * flip)
sinplot()#calling sinplot
#box plot
np.random.seed(sum(map(ord, "abhishek")))#here we are just seeding as to get same plot everytime we run the code
sns.set_style('whitegrid')
data = np.random.normal(size=(20, 6)) + np.arange(6)/2#here data is having 20 rows 6 columns
sns.boxplot(data=data)
#distplot
sns.distplot(x)
#count plot
sns.countplot(x='Survived', data=trainData, hue='Sex', palette='Blues_r')
#simple histogram
trainData.Age.plot(kind='hist')#histogram of Age column from trainData
#sns Facetgrid
g = sns.FacetGrid(trainData, col='Survived')
g.map(sns.distplot, 'Age', bins=20)#here we are creating distplot with 20 bins for column Age based on column Survived, column survived is having 2 values 0 and 100
#sns pairplot or scatterplot
sns.pairplot(dataset, size=6, hue='column_To_Color', x_vars='X_col',y_vars='y_col')
plt.show()
#basic bar chart
x = [1, 2, 3, 4, 5]
y = [1, 2, 3, 4, 5]
plt.bar(x, y, label = 'x')
plt.title('bar chart')
plt.xlabel('x')
plt.ylabel('y')
plt.legend()
plt.show()
#basic scatter plot
x = [1, 2, 3, 4, 5]
y = [1, 2, 3, 4, 5]
plt.scatter([],[], marker = '*', s = 50, label = 'x', color ='r')#to display small legend as for size=150(next line) the legend will be very big
plt.scatter(x, y, marker = '*', s = 150, color = 'r')
plt.title('bar chart')
plt.xlabel('x')
plt.ylabel('y')
plt.legend()
plt.show()
#basic histogram
import numpy as np
x = np.random.randint(10, 100, 100)
bins = [n for n in np.arange(10, 110, 10)]
print(bins)
plt.hist(x, bins=bins, histtype='bar', rwidth=0.8)
#basic stackplot
days = [1, 2, 3, 4, 5]
sleeping = [6, 5, 4, 8, 12]
eating = [3, 6, 3, 4, 2]
playing = [5, 3, 2, 4, 3]
working = [10, 10, 15, 8, 7]
plt.stackplot(days, sleeping, eating, playing, working,
colors = ['m', 'c', 'r', 'b'])
#for fake legends
plt.plot([],[], label = 'sleep', color='m', linewidth=5)
plt.plot([],[], label = 'eat', color='c', linewidth=5)
plt.plot([],[], label = 'play', color='r', linewidth=5)
plt.plot([],[], label = 'work', color='b', linewidth=5)
plt.legend()
#pie chart with popped out slice
#pie chart with popped out slice
slice = np.array([7, 2, 2, 13])
activities = ['sleep', 'eat', 'play', 'work']
def my_autopct(pct, slice):#this function is used to display values
total = slice.sum()
val = pct*total/100
return "{:.1f}% ({:d})".format(pct, val)
plt.pie(slice, labels=activities,
colors = ['m', 'c', 'r', 'b'],
startangle=90,
shadow=True,
explode=(0, 0.1, 0, 0),#for which slice to pop out
autopct=lambda pct: func(pct, slice),
textprops=dict(color="w"))
#autopct = '%1.1f%%'
plt.show()
#plot date
plt.plot_date(date, closep, '-')
plt.show()
#defining subplot and figure and axis rotation
fig = plt.figure(1)#here we define a figure with number 1
ax1 = plt.subplot2grid((1,1),(0,0))#here we define a grid of size 1*1 and ax1 plot will be at position 0,0, as we have only 1*1 space we only have one location 0,0
ax1.plot_date(date, closep, '-')
plt.xlabel('date')
plt.ylabel('price')
plt.title('stocks')
plt.subplots_adjust(left=0.09, bottom=.2)
for label in ax1.xaxis.get_ticklabels():
label.set_rotation(45)#for rotating x ticklabels
ax1.grid(True, color = 'g', linestyle='-')#for grid
plt.show()
#formatting plot using fill between and other formatting
fig = plt.figure(1)#here we define a figure with number 1
ax1 = plt.subplot2grid((1,1),(0,0))
ax1.plot_date(date, closep, '-', label='price')
ax1.fill_between(date, closep[0], closep, where=closep>closep[0], color='g', alpha=0.3)
ax1.fill_between(date, closep[0], closep, where=closep<closep[0], color='r', alpha=0.3)
ax1.plot([], [], linewidth=5, label='loss', color='r', alpha=0.5)
ax1.plot([], [], linewidth=5, label='gain', color='g', alpha=0.5)
plt.xlabel('date')
plt.ylabel('price')
ax1.xaxis.label.set_color('c')#label coloring
ax1.yaxis.label.set_color('r')
ax1.set_yticks([0, 125, 250, 375, 600])#y points
plt.title('stocks')
plt.subplots_adjust(left=0.10, bottom=.2)
for label in ax1.xaxis.get_ticklabels():
label.set_rotation(45)
ax1.grid(True, color='g', linestyle='--')
ax1.axhline(closep[0], color='k', linewidth=3)#for horizontal line at close[0]
ax1.spines['left'].set_color('c')
ax1.spines['right'].set_visible(False)
ax1.spines['top'].set_visible(False)
ax1.spines['left'].set_linewidth(3)
plt.legend()
plt.show()
#for finance plots we have candlestic plot
from matplotlib.finance import candlestick_ohlc#this is deprecated so you can use mpl_finance instead
#different plot styles
from matplotlib import style
style.use('ggplot')
style.use('fivethirtyeight')
style.use('dark_background')
#we can use many more
print(plt.style.available)#you can get all the available styles from here
#live plot, animated plot
import matplotlib.pyplot as plt
import matplotlib.animation as animation
from matplotlib import style
style.use('fivethirtyeight')
fig = plt.figure()
ax1 = fig.add_subplot(1,1,1)
def animate(i):
graph_data = open(r'C:\Users\avishek\Downloads\DataFiles\live_data.txt','r').read()#this data in the source file we can keep changing and the plot will keep updating
lines = graph_data.split('\n')
xs = []
ys = []
for line in lines:
if len(line) > 1:#to check if line has some data and is not null
x, y = line.split(',')
xs.append(x)
ys.append(y)
ax1.clear()#clear the previously draw plot
ax1.plot(xs, ys)
ani = animation.FuncAnimation(fig, animate, interval=1000)#its like call this function after every second
plt.show()
#annotation in plots
fig, ax = plt.subplots()#define subplots it returns figure and axis
t = np.arange(0.0, 5.0, 0.01)
s = np.cos(2*np.pi*t)
line, = ax.plot(t, s, lw=2)
ax.annotate('local max', xy=(2, 1), xytext=(3, 1.5),
arrowprops=dict(facecolor='black', shrink=0.05),
)
ax.set_ylim(-2, 2)#setting y axis limit, the values shown on y axis will be from -2 to 2 same we can do set_xlim
plt.show()
#text in plots
font = {'family': 'serif','color': 'darkred','weight': 'normal','size': 16,}
df1.plot(kind='scatter',x='CITY (kWh/100 km)', y='CITY (Le/100 km)')
plt.text(16,2.6,'scatter plot', fontdict=font)
plt.show()
#more subplot2grid
fig = plt.figure()
#here we define a plot which spans 6rows and one column
ax1 = plt.subplot2grid((6,1),(0,0), rowspan=2, colspan=1)#ax1 is defined as row 0 to row 1, this spans 2 rows
ax2 = plt.subplot2grid((6,1),(2,0), rowspan=3, colspan=1)#ax2 is defined as row 2 to row 5, this spans 3 rows
ax3 = plt.subplot2grid((6,1),(5,0), rowspan=1, colspan=1)#ax1 is defined as row 6 to row 6, this spans 1 row
ax1.plot(x, y)
ax2.plot(x, y1)
ax3.plot(x, y2)
plt.show()
#3dplots
from mpl_toolkits.mplot3d import axes3d
import matplotlib.pyplot as plt
from matplotlib import style
style.use('fivethirtyeight')
fig = plt.figure()
ax1 = fig.add_subplot(111, projection='3d')
x = [1,2,3,4,5,6,7,8,9,10]
y = [5,6,7,8,2,5,6,3,7,2]
z = [1,2,6,3,2,7,3,3,7,2]
ax1.plot_wireframe(x,y,z, color='k')
ax1.set_xlabel('x axis')
ax1.set_ylabel('y axis')
ax1.set_zlabel('z axis')
plt.show()
#figsize
fig = plt.figure(figsize=(8,8))#define a figure of size 8, 8
ax = fig.gca()#getting the current axis
data[['engine-size','fuel-type']].boxplot(by = 'fuel-type', ax=ax)
#violin plot
sns.violinplot(x='fuel-type', y='engine-size', data=aData)
#linear model plot lm plot
sns.lmplot(x='city-mpg', y='price', data=aData, hue='fuel-type', palette='Set2')
#joint plot
sns.jointplot('engine-size', 'price', data=aData, alpha=0.3)
#pairplot
#when not mentioned any x or y it will create pairs of all the numeric columns and on diagonal plot the histograms
import seaborn as sns; sns.set(style="ticks", color_codes=True)
iris = sns.load_dataset("iris")
g = sns.pairplot(iris)
#subplot
x = np.random.rand(10)
y = np.random.rand(10)
z = np.sqrt(x**2 + y**2)
plt.subplot(321)#3 by 2 grid and in that 1st graph
plt.scatter(x, y, s=80, c=z, marker=">")
plt.subplot(322)#3 by 2 grid and in that 2nd graph
plt.scatter(x, y, s=80, c=z, marker=(5, 0))
verts = np.array([[-1, -1], [1, -1], [1, 1], [-1, -1]])
plt.subplot(323)#3 by 2 grid and in that 3rd graph
plt.scatter(x, y, s=80, c=z, marker=verts)
plt.subplot(324)#3 by 2 grid and in that 4th graph
plt.scatter(x, y, s=80, c=z, marker=(5, 1))
plt.subplot(325#3 by 2 grid and in that 5th graph
plt.scatter(x, y, s=80, c=z, marker='+')
plt.subplot(326)#3 by 2 grid and in that 6th graph
plt.scatter(x, y, s=80, c=z, marker=(5, 2))
plt.show()
#subplots
data = {'apples': 10, 'oranges': 15, 'lemons': 5, 'limes': 20}
names = list(data.keys())
values = list(data.values())
fig, axs = plt.subplots(1, 3, figsize=(9, 3), sharey=True)#also here we are saying to share the y axis so all the three plots will have a common y axis
axs[0].bar(names, values)
axs[1].scatter(names, values)
axs[2].plot(names, values)
fig.suptitle('Categorical Plotting')
#get months from date and plot based on that
data.groupby(data['Date field'].dt.strftime('%B')).size().plot(kind = 'bar')
#imageshow
plt.imshow(image.reshape((28,28)), cmap=plt.cm.gray_r)
#figsize
plt.figure(figsize=(6, 4))#here 6 is width and 4 is heigth
#figaspect
fig = plt.figure(figsize=plt.figaspect(0.5))#aspect 0.5 means width is 2wice the height
ax = fig.add_subplot(111)
ax.set(xlim=[0.5, 4.5], ylim=[-2, 8], title='example', ylabel='y-axis', xlabel='x-axis')
plt.show()
#basic subplots
fig, ax = plt.subplots(nrows=2, ncols=2)
ax[0,0].set(title='upper left')
ax[0,1].set(title='upper right')
ax[1,0].set(title='lower left')
ax[1,1].set(title='lower right')
#to itertate over the axes
for x in ax.flat:#ax is an array of axis
x.set(xticks=[], yticks=[])#set xtics and ytics to blank
plt.show()
#another subplots example
plt.style.use('classic')
fig, ax = plt.subplots(nrows=3)
fig.suptitle('maintitle')#super title
x=np.linspace(0, 10, 100)
y1, y2, y3=np.cos(x), np.cos(x+1), np.cos(x+2)
names = ['signal 1', 'signal 2', 'signal 3']
for a, y, name in zip(ax, [y1, y2, y3], names):
a.plot(x, y)
a.set(xticks=[], yticks=[], title=name)
plt.show()
#formatting bar chart bar
plt.style.use('classic')
np.random.seed(1)
fig, ax = plt.subplots(ncols=2, figsize=plt.figaspect(0.5))
x = np.arange(5)
y = np.random.randn(5)
#if you want to modify the properties we can captute like below
vert_bars = ax[0].bar(x, y, color='lightblue', align='center')
horiz_bars = ax[1].barh(x, y, color='lightblue', align='center')
ax[0].axhline(0, color='gray', linewidth=2)
ax[1].axvline(0, color='gray', linewidth=2)
#try hard you will understand, just think what is in vert_bars
for bar, height in zip(vert_bars, y):#we can iterate over individual bars
if height < 0:
bar.set(edgecolor='darkred', color='salmon', linewidth=3)
print(vert_bars)
plt.show()
#using fill between
fig, ax = plt.subplots()
x = np.linspace(0,10,200)
y1 = 2 * x + 1
y2 = 3 * x + 1
y_mean = 0.5 * x * np.cos(2 * x) + 2.5* x + 1.1
ax.fill_between(x, y1, y2, color='yellow')
ax.plot(x, y_mean, color='black')
plt.show()
#you can create the same plot as above using data_obj
fig, ax = plt.subplots()
x = np.linspace(0,10,200)
data_obj = { 'x': x, #here we define all the data to be use in chart in form of dictionary
'y1': 2 * x + 1,
'y2': 3 * x + 1,
'mean': 0.5 * x * np.cos(2 * x) + 2.5* x + 1.1
}
ax.fill_between('x', 'y1', 'y2', color='yellow', data=data_obj)
ax.plot('x', 'mean', color='black', data=data_obj)
plt.show()
#getting sample data and creating color map and use cmap
from matplotlib.cbook import get_sample_data
data = np.load(get_sample_data(r'axes_grid/bivariate_normal.npy'))
fig, ax = plt.subplots()
im = ax.imshow(data, cmap='gist_earth')
fig.colorbar(im)#we are adding a colorbar to a figure not the axis
plt.show()
#adding colorbar axis
np.random.seed(1)
fig, ax = plt.subplots(ncols=3, figsize=plt.figaspect(0.5))
plt.style.use('classic')
data1 = np.random.random((10,10))
data2 = 2*np.random.random((10,10))
data3 = 3*np.random.random((10,10))
fig.tight_layout() #makes the subplots fill up the figure
cax = fig.add_axes([0.25, 0.1, 0.55, 0.03])
for a,data in zip(ax, [data1, data2, data3]):
im = a.imshow(data, vmin=0, vmax=3, interpolation='nearest')
fig.colorbar(im, cax=cax, orientation='horizontal')#here cax is colorbar axis and is defined above
plt.show()
#markers in matplotlib
xs, ys = np.mgrid[:4, 9:0:-1]
markers = [".", "+", ",", "x", "o", "D", "d", "", "8", "s", "p", "*", "|", "_", "h", "H", 0, 4, "<", "3",
1, 5, ">", "4", 2, 6, "^", "2", 3, 7, "v", "1", "None", None, " ", ""]
descripts = ["point", "plus", "pixel", "cross", "circle", "diamond", "thin diamond", "",
"octagon", "square", "pentagon", "star", "vertical bar", "horizontal bar", "hexagon 1", "hexagon 2",
"tick left", "caret left", "triangle left", "tri left", "tick right", "caret right", "triangle right", "tri right",
"tick up", "caret up", "triangle up", "tri up", "tick down", "caret down", "triangle down", "tri down",
"Nothing", "Nothing", "Nothing", "Nothing"]
fig, ax = plt.subplots(1, 1, figsize=(10, 6))
for x, y, m, d in zip(xs.T.flat, ys.T.flat, markers, descripts):
ax.scatter(x, y, marker=m, s=100, color='red')
ax.text(x + 0.1, y - 0.1, d, size=14)
ax.set_axis_off()
plt.show()
#barchart edge color
fig, ax = plt.subplots()
ax.bar([1,2,3,4],[10,25,15,20], ls='dashed', ec='red', linewidth=4)#ec can be written as edgecolor also, ecolor stands for error color
plt.show()
#marker coloring
fig, ax = plt.subplots()
t = np.arange(0.0, 5.0, 0.1)
a = np.exp(-t) * np.cos(2*np.pi*t)
#plt.plot(t, a, '--r', t, a, 'dy', mec='g')
plt.plot(t, a, 'r:D', mfc='y', mec='g')#D big diamond, d small diamond, mec=marker edge color, mfc=marker face color
plt.show()
#cmaps present
cmaps = [('Perceptually Uniform Sequential', [
'viridis', 'plasma', 'inferno', 'magma']),
('Sequential', [
'Greys', 'Purples', 'Blues', 'Greens', 'Oranges', 'Reds',
'YlOrBr', 'YlOrRd', 'OrRd', 'PuRd', 'RdPu', 'BuPu',
'GnBu', 'PuBu', 'YlGnBu', 'PuBuGn', 'BuGn', 'YlGn']),
('Sequential (2)', [
'binary', 'gist_yarg', 'gist_gray', 'gray', 'bone', 'pink',
'spring', 'summer', 'autumn', 'winter', 'cool', 'Wistia',
'hot', 'afmhot', 'gist_heat', 'copper']),
('Diverging', [
'PiYG', 'PRGn', 'BrBG', 'PuOr', 'RdGy', 'RdBu',
'RdYlBu', 'RdYlGn', 'Spectral', 'coolwarm', 'bwr', 'seismic']),
('Qualitative', [
'Pastel1', 'Pastel2', 'Paired', 'Accent',
'Dark2', 'Set1', 'Set2', 'Set3',
'tab10', 'tab20', 'tab20b', 'tab20c']),
('Miscellaneous', [
'flag', 'prism', 'ocean', 'gist_earth', 'terrain', 'gist_stern',
'gnuplot', 'gnuplot2', 'CMRmap', 'cubehelix', 'brg', 'hsv',
'gist_rainbow', 'rainbow', 'jet', 'nipy_spectral', 'gist_ncar'])]
# Turn off *all* ticks & spines
for ax in axes:
ax.set_axis_off()
#mathtext, latent text
plt.scatter([1, 2, 3, 4], [4, 3, 2, 1])
plt.title(r'$\sigma_i=15$', fontsize=20)#see the title
plt.show()
#mpl.rc, below instead of using the predefined color or line style it will cycle through the one defined in rc without having to explicitly mention
import matplotlib as mpl
from matplotlib.rcsetup import cycler
mpl.rc('axes', prop_cycle=cycler('color', 'rgc') +
cycler('lw', [1, 2, 4]) +
cycler('linestyle', ['-', '-.', ':']))#by default only color cycles ,this is for other property cycling
t = np.arange(0.0, 5.0, 0.2)
plt.plot(t, t)
plt.plot(t, t**2)
plt.plot(t, t**3)
plt.show()#rc might stand for run and configure
#hide the boundry line
fig, ax = plt.subplots()
ax.plot([-2, 2, 3, 4], [-10, 20, 25, 5])
ax.spines['top'].set_visible(False)#this is used to hide the top line of the box
ax.xaxis.set_ticks_position('bottom') # no ticklines at the top
ax.spines['right'].set_visible(False)
ax.yaxis.set_ticks_position('left') # no ticklines on the right
plt.show()
#box plot
np.random.seed(sum(map(ord, "abhishek")))#here we are just seeding as to get same plot everytime we run the code
sns.set_style('whitegrid')
data = np.random.normal(size=(20, 6)) + np.arange(6)/2#here data is having 20 rows 6 columns
sns.boxplot(data=data)
#distplot
sns.distplot(x)
#count plot
sns.countplot(x='Survived', data=trainData, hue='Sex', palette='Blues_r')
#simple histogram
trainData.Age.plot(kind='hist')#histogram of Age column from trainData
#sns Facetgrid
g = sns.FacetGrid(trainData, col='Survived')
g.map(sns.distplot, 'Age', bins=20)#here we are creating distplot with 20 bins for column Age based on column Survived, column survived is having 2 values 0 and 100
#sns pairplot or scatterplot
sns.pairplot(dataset, size=6, hue='column_To_Color', x_vars='X_col',y_vars='y_col')
plt.show()
#basic bar chart
x = [1, 2, 3, 4, 5]
y = [1, 2, 3, 4, 5]
plt.bar(x, y, label = 'x')
plt.title('bar chart')
plt.xlabel('x')
plt.ylabel('y')
plt.legend()
plt.show()
#basic scatter plot
x = [1, 2, 3, 4, 5]
y = [1, 2, 3, 4, 5]
plt.scatter([],[], marker = '*', s = 50, label = 'x', color ='r')#to display small legend as for size=150(next line) the legend will be very big
plt.scatter(x, y, marker = '*', s = 150, color = 'r')
plt.title('bar chart')
plt.xlabel('x')
plt.ylabel('y')
plt.legend()
plt.show()
#basic histogram
import numpy as np
x = np.random.randint(10, 100, 100)
bins = [n for n in np.arange(10, 110, 10)]
print(bins)
plt.hist(x, bins=bins, histtype='bar', rwidth=0.8)
#basic stackplot
days = [1, 2, 3, 4, 5]
sleeping = [6, 5, 4, 8, 12]
eating = [3, 6, 3, 4, 2]
playing = [5, 3, 2, 4, 3]
working = [10, 10, 15, 8, 7]
plt.stackplot(days, sleeping, eating, playing, working,
colors = ['m', 'c', 'r', 'b'])
#for fake legends
plt.plot([],[], label = 'sleep', color='m', linewidth=5)
plt.plot([],[], label = 'eat', color='c', linewidth=5)
plt.plot([],[], label = 'play', color='r', linewidth=5)
plt.plot([],[], label = 'work', color='b', linewidth=5)
plt.legend()
#pie chart with popped out slice
#pie chart with popped out slice
slice = np.array([7, 2, 2, 13])
activities = ['sleep', 'eat', 'play', 'work']
def my_autopct(pct, slice):#this function is used to display values
total = slice.sum()
val = pct*total/100
return "{:.1f}% ({:d})".format(pct, val)
plt.pie(slice, labels=activities,
colors = ['m', 'c', 'r', 'b'],
startangle=90,
shadow=True,
explode=(0, 0.1, 0, 0),#for which slice to pop out
autopct=lambda pct: func(pct, slice),
textprops=dict(color="w"))
#autopct = '%1.1f%%'
plt.show()
#plot date
plt.plot_date(date, closep, '-')
plt.show()
#defining subplot and figure and axis rotation
fig = plt.figure(1)#here we define a figure with number 1
ax1 = plt.subplot2grid((1,1),(0,0))#here we define a grid of size 1*1 and ax1 plot will be at position 0,0, as we have only 1*1 space we only have one location 0,0
ax1.plot_date(date, closep, '-')
plt.xlabel('date')
plt.ylabel('price')
plt.title('stocks')
plt.subplots_adjust(left=0.09, bottom=.2)
for label in ax1.xaxis.get_ticklabels():
label.set_rotation(45)#for rotating x ticklabels
ax1.grid(True, color = 'g', linestyle='-')#for grid
plt.show()
#formatting plot using fill between and other formatting
fig = plt.figure(1)#here we define a figure with number 1
ax1 = plt.subplot2grid((1,1),(0,0))
ax1.plot_date(date, closep, '-', label='price')
ax1.fill_between(date, closep[0], closep, where=closep>closep[0], color='g', alpha=0.3)
ax1.fill_between(date, closep[0], closep, where=closep<closep[0], color='r', alpha=0.3)
ax1.plot([], [], linewidth=5, label='loss', color='r', alpha=0.5)
ax1.plot([], [], linewidth=5, label='gain', color='g', alpha=0.5)
plt.xlabel('date')
plt.ylabel('price')
ax1.xaxis.label.set_color('c')#label coloring
ax1.yaxis.label.set_color('r')
ax1.set_yticks([0, 125, 250, 375, 600])#y points
plt.title('stocks')
plt.subplots_adjust(left=0.10, bottom=.2)
for label in ax1.xaxis.get_ticklabels():
label.set_rotation(45)
ax1.grid(True, color='g', linestyle='--')
ax1.axhline(closep[0], color='k', linewidth=3)#for horizontal line at close[0]
ax1.spines['left'].set_color('c')
ax1.spines['right'].set_visible(False)
ax1.spines['top'].set_visible(False)
ax1.spines['left'].set_linewidth(3)
plt.legend()
plt.show()
#for finance plots we have candlestic plot
from matplotlib.finance import candlestick_ohlc#this is deprecated so you can use mpl_finance instead
#different plot styles
from matplotlib import style
style.use('ggplot')
style.use('fivethirtyeight')
style.use('dark_background')
#we can use many more
print(plt.style.available)#you can get all the available styles from here
#live plot, animated plot
import matplotlib.pyplot as plt
import matplotlib.animation as animation
from matplotlib import style
style.use('fivethirtyeight')
fig = plt.figure()
ax1 = fig.add_subplot(1,1,1)
def animate(i):
graph_data = open(r'C:\Users\avishek\Downloads\DataFiles\live_data.txt','r').read()#this data in the source file we can keep changing and the plot will keep updating
lines = graph_data.split('\n')
xs = []
ys = []
for line in lines:
if len(line) > 1:#to check if line has some data and is not null
x, y = line.split(',')
xs.append(x)
ys.append(y)
ax1.clear()#clear the previously draw plot
ax1.plot(xs, ys)
ani = animation.FuncAnimation(fig, animate, interval=1000)#its like call this function after every second
plt.show()
#annotation in plots
fig, ax = plt.subplots()#define subplots it returns figure and axis
t = np.arange(0.0, 5.0, 0.01)
s = np.cos(2*np.pi*t)
line, = ax.plot(t, s, lw=2)
ax.annotate('local max', xy=(2, 1), xytext=(3, 1.5),
arrowprops=dict(facecolor='black', shrink=0.05),
)
ax.set_ylim(-2, 2)#setting y axis limit, the values shown on y axis will be from -2 to 2 same we can do set_xlim
plt.show()
#text in plots
font = {'family': 'serif','color': 'darkred','weight': 'normal','size': 16,}
df1.plot(kind='scatter',x='CITY (kWh/100 km)', y='CITY (Le/100 km)')
plt.text(16,2.6,'scatter plot', fontdict=font)
plt.show()
#more subplot2grid
fig = plt.figure()
#here we define a plot which spans 6rows and one column
ax1 = plt.subplot2grid((6,1),(0,0), rowspan=2, colspan=1)#ax1 is defined as row 0 to row 1, this spans 2 rows
ax2 = plt.subplot2grid((6,1),(2,0), rowspan=3, colspan=1)#ax2 is defined as row 2 to row 5, this spans 3 rows
ax3 = plt.subplot2grid((6,1),(5,0), rowspan=1, colspan=1)#ax1 is defined as row 6 to row 6, this spans 1 row
ax1.plot(x, y)
ax2.plot(x, y1)
ax3.plot(x, y2)
plt.show()
#3dplots
from mpl_toolkits.mplot3d import axes3d
import matplotlib.pyplot as plt
from matplotlib import style
style.use('fivethirtyeight')
fig = plt.figure()
ax1 = fig.add_subplot(111, projection='3d')
x = [1,2,3,4,5,6,7,8,9,10]
y = [5,6,7,8,2,5,6,3,7,2]
z = [1,2,6,3,2,7,3,3,7,2]
ax1.plot_wireframe(x,y,z, color='k')
ax1.set_xlabel('x axis')
ax1.set_ylabel('y axis')
ax1.set_zlabel('z axis')
plt.show()
#figsize
fig = plt.figure(figsize=(8,8))#define a figure of size 8, 8
ax = fig.gca()#getting the current axis
data[['engine-size','fuel-type']].boxplot(by = 'fuel-type', ax=ax)
#violin plot
sns.violinplot(x='fuel-type', y='engine-size', data=aData)
#linear model plot lm plot
sns.lmplot(x='city-mpg', y='price', data=aData, hue='fuel-type', palette='Set2')
#joint plot
sns.jointplot('engine-size', 'price', data=aData, alpha=0.3)
#pairplot
#when not mentioned any x or y it will create pairs of all the numeric columns and on diagonal plot the histograms
import seaborn as sns; sns.set(style="ticks", color_codes=True)
iris = sns.load_dataset("iris")
g = sns.pairplot(iris)
#subplot
x = np.random.rand(10)
y = np.random.rand(10)
z = np.sqrt(x**2 + y**2)
plt.subplot(321)#3 by 2 grid and in that 1st graph
plt.scatter(x, y, s=80, c=z, marker=">")
plt.subplot(322)#3 by 2 grid and in that 2nd graph
plt.scatter(x, y, s=80, c=z, marker=(5, 0))
verts = np.array([[-1, -1], [1, -1], [1, 1], [-1, -1]])
plt.subplot(323)#3 by 2 grid and in that 3rd graph
plt.scatter(x, y, s=80, c=z, marker=verts)
plt.subplot(324)#3 by 2 grid and in that 4th graph
plt.scatter(x, y, s=80, c=z, marker=(5, 1))
plt.subplot(325#3 by 2 grid and in that 5th graph
plt.scatter(x, y, s=80, c=z, marker='+')
plt.subplot(326)#3 by 2 grid and in that 6th graph
plt.scatter(x, y, s=80, c=z, marker=(5, 2))
plt.show()
#subplots
data = {'apples': 10, 'oranges': 15, 'lemons': 5, 'limes': 20}
names = list(data.keys())
values = list(data.values())
fig, axs = plt.subplots(1, 3, figsize=(9, 3), sharey=True)#also here we are saying to share the y axis so all the three plots will have a common y axis
axs[0].bar(names, values)
axs[1].scatter(names, values)
axs[2].plot(names, values)
fig.suptitle('Categorical Plotting')
#get months from date and plot based on that
data.groupby(data['Date field'].dt.strftime('%B')).size().plot(kind = 'bar')
#imageshow
plt.imshow(image.reshape((28,28)), cmap=plt.cm.gray_r)
#figsize
plt.figure(figsize=(6, 4))#here 6 is width and 4 is heigth
#figaspect
fig = plt.figure(figsize=plt.figaspect(0.5))#aspect 0.5 means width is 2wice the height
ax = fig.add_subplot(111)
ax.set(xlim=[0.5, 4.5], ylim=[-2, 8], title='example', ylabel='y-axis', xlabel='x-axis')
plt.show()
#basic subplots
fig, ax = plt.subplots(nrows=2, ncols=2)
ax[0,0].set(title='upper left')
ax[0,1].set(title='upper right')
ax[1,0].set(title='lower left')
ax[1,1].set(title='lower right')
#to itertate over the axes
for x in ax.flat:#ax is an array of axis
x.set(xticks=[], yticks=[])#set xtics and ytics to blank
plt.show()
#another subplots example
plt.style.use('classic')
fig, ax = plt.subplots(nrows=3)
fig.suptitle('maintitle')#super title
x=np.linspace(0, 10, 100)
y1, y2, y3=np.cos(x), np.cos(x+1), np.cos(x+2)
names = ['signal 1', 'signal 2', 'signal 3']
for a, y, name in zip(ax, [y1, y2, y3], names):
a.plot(x, y)
a.set(xticks=[], yticks=[], title=name)
plt.show()
#formatting bar chart bar
plt.style.use('classic')
np.random.seed(1)
fig, ax = plt.subplots(ncols=2, figsize=plt.figaspect(0.5))
x = np.arange(5)
y = np.random.randn(5)
#if you want to modify the properties we can captute like below
vert_bars = ax[0].bar(x, y, color='lightblue', align='center')
horiz_bars = ax[1].barh(x, y, color='lightblue', align='center')
ax[0].axhline(0, color='gray', linewidth=2)
ax[1].axvline(0, color='gray', linewidth=2)
#try hard you will understand, just think what is in vert_bars
for bar, height in zip(vert_bars, y):#we can iterate over individual bars
if height < 0:
bar.set(edgecolor='darkred', color='salmon', linewidth=3)
print(vert_bars)
plt.show()
#using fill between
fig, ax = plt.subplots()
x = np.linspace(0,10,200)
y1 = 2 * x + 1
y2 = 3 * x + 1
y_mean = 0.5 * x * np.cos(2 * x) + 2.5* x + 1.1
ax.fill_between(x, y1, y2, color='yellow')
ax.plot(x, y_mean, color='black')
plt.show()
#you can create the same plot as above using data_obj
fig, ax = plt.subplots()
x = np.linspace(0,10,200)
data_obj = { 'x': x, #here we define all the data to be use in chart in form of dictionary
'y1': 2 * x + 1,
'y2': 3 * x + 1,
'mean': 0.5 * x * np.cos(2 * x) + 2.5* x + 1.1
}
ax.fill_between('x', 'y1', 'y2', color='yellow', data=data_obj)
ax.plot('x', 'mean', color='black', data=data_obj)
plt.show()
#getting sample data and creating color map and use cmap
from matplotlib.cbook import get_sample_data
data = np.load(get_sample_data(r'axes_grid/bivariate_normal.npy'))
fig, ax = plt.subplots()
im = ax.imshow(data, cmap='gist_earth')
fig.colorbar(im)#we are adding a colorbar to a figure not the axis
plt.show()
#adding colorbar axis
np.random.seed(1)
fig, ax = plt.subplots(ncols=3, figsize=plt.figaspect(0.5))
plt.style.use('classic')
data1 = np.random.random((10,10))
data2 = 2*np.random.random((10,10))
data3 = 3*np.random.random((10,10))
fig.tight_layout() #makes the subplots fill up the figure
cax = fig.add_axes([0.25, 0.1, 0.55, 0.03])
for a,data in zip(ax, [data1, data2, data3]):
im = a.imshow(data, vmin=0, vmax=3, interpolation='nearest')
fig.colorbar(im, cax=cax, orientation='horizontal')#here cax is colorbar axis and is defined above
plt.show()
#markers in matplotlib
xs, ys = np.mgrid[:4, 9:0:-1]
markers = [".", "+", ",", "x", "o", "D", "d", "", "8", "s", "p", "*", "|", "_", "h", "H", 0, 4, "<", "3",
1, 5, ">", "4", 2, 6, "^", "2", 3, 7, "v", "1", "None", None, " ", ""]
descripts = ["point", "plus", "pixel", "cross", "circle", "diamond", "thin diamond", "",
"octagon", "square", "pentagon", "star", "vertical bar", "horizontal bar", "hexagon 1", "hexagon 2",
"tick left", "caret left", "triangle left", "tri left", "tick right", "caret right", "triangle right", "tri right",
"tick up", "caret up", "triangle up", "tri up", "tick down", "caret down", "triangle down", "tri down",
"Nothing", "Nothing", "Nothing", "Nothing"]
fig, ax = plt.subplots(1, 1, figsize=(10, 6))
for x, y, m, d in zip(xs.T.flat, ys.T.flat, markers, descripts):
ax.scatter(x, y, marker=m, s=100, color='red')
ax.text(x + 0.1, y - 0.1, d, size=14)
ax.set_axis_off()
plt.show()
#barchart edge color
fig, ax = plt.subplots()
ax.bar([1,2,3,4],[10,25,15,20], ls='dashed', ec='red', linewidth=4)#ec can be written as edgecolor also, ecolor stands for error color
plt.show()
#marker coloring
fig, ax = plt.subplots()
t = np.arange(0.0, 5.0, 0.1)
a = np.exp(-t) * np.cos(2*np.pi*t)
#plt.plot(t, a, '--r', t, a, 'dy', mec='g')
plt.plot(t, a, 'r:D', mfc='y', mec='g')#D big diamond, d small diamond, mec=marker edge color, mfc=marker face color
plt.show()
#cmaps present
cmaps = [('Perceptually Uniform Sequential', [
'viridis', 'plasma', 'inferno', 'magma']),
('Sequential', [
'Greys', 'Purples', 'Blues', 'Greens', 'Oranges', 'Reds',
'YlOrBr', 'YlOrRd', 'OrRd', 'PuRd', 'RdPu', 'BuPu',
'GnBu', 'PuBu', 'YlGnBu', 'PuBuGn', 'BuGn', 'YlGn']),
('Sequential (2)', [
'binary', 'gist_yarg', 'gist_gray', 'gray', 'bone', 'pink',
'spring', 'summer', 'autumn', 'winter', 'cool', 'Wistia',
'hot', 'afmhot', 'gist_heat', 'copper']),
('Diverging', [
'PiYG', 'PRGn', 'BrBG', 'PuOr', 'RdGy', 'RdBu',
'RdYlBu', 'RdYlGn', 'Spectral', 'coolwarm', 'bwr', 'seismic']),
('Qualitative', [
'Pastel1', 'Pastel2', 'Paired', 'Accent',
'Dark2', 'Set1', 'Set2', 'Set3',
'tab10', 'tab20', 'tab20b', 'tab20c']),
('Miscellaneous', [
'flag', 'prism', 'ocean', 'gist_earth', 'terrain', 'gist_stern',
'gnuplot', 'gnuplot2', 'CMRmap', 'cubehelix', 'brg', 'hsv',
'gist_rainbow', 'rainbow', 'jet', 'nipy_spectral', 'gist_ncar'])]
# Turn off *all* ticks & spines
for ax in axes:
ax.set_axis_off()
#mathtext, latent text
plt.scatter([1, 2, 3, 4], [4, 3, 2, 1])
plt.title(r'$\sigma_i=15$', fontsize=20)#see the title
plt.show()
#mpl.rc, below instead of using the predefined color or line style it will cycle through the one defined in rc without having to explicitly mention
import matplotlib as mpl
from matplotlib.rcsetup import cycler
mpl.rc('axes', prop_cycle=cycler('color', 'rgc') +
cycler('lw', [1, 2, 4]) +
cycler('linestyle', ['-', '-.', ':']))#by default only color cycles ,this is for other property cycling
t = np.arange(0.0, 5.0, 0.2)
plt.plot(t, t)
plt.plot(t, t**2)
plt.plot(t, t**3)
plt.show()#rc might stand for run and configure
#hide the boundry line
fig, ax = plt.subplots()
ax.plot([-2, 2, 3, 4], [-10, 20, 25, 5])
ax.spines['top'].set_visible(False)#this is used to hide the top line of the box
ax.xaxis.set_ticks_position('bottom') # no ticklines at the top
ax.spines['right'].set_visible(False)
ax.yaxis.set_ticks_position('left') # no ticklines on the right
plt.show()
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