import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
from mpl_toolkits.mplot3d import Axes3D
import seaborn as sns
import os
from sklearn.decomposition import PCA
import imageiodf = sns.load_dataset('iris')my_dpi=96
plt.figure(figsize=(480/my_dpi, 480/my_dpi), dpi=my_dpi)# Keep the 'specie' column appart + make it numeric for coloring
df['species']=pd.Categorical(df['species'])
my_color=df['species'].cat.codes
df = df.drop('species', 1)# Run The PCA
pca = PCA(n_components=3)
pca.fit(df)# Store results of PCA in a data frame
result=pd.DataFrame(pca.transform(df), columns=['PCA%i' % i for i in range(3)], index=df.index)# 20 plots, for 20 different angles
for angle in range(70,210,2):# Plot initialisationfig = plt.figure()ax = fig.add_subplot(111, projection='3d')ax.scatter(result['PCA0'], result['PCA1'], result['PCA2'], c=my_color, cmap="Set2_r", s=60)# make simple, bare axis lines through space:xAxisLine = ((min(result['PCA0']), max(result['PCA0'])), (0, 0), (0,0))ax.plot(xAxisLine[0], xAxisLine[1], xAxisLine[2], 'r')yAxisLine = ((0, 0), (min(result['PCA1']), max(result['PCA1'])), (0,0))ax.plot(yAxisLine[0], yAxisLine[1], yAxisLine[2], 'r')zAxisLine = ((0, 0), (0,0), (min(result['PCA2']), max(result['PCA2'])))ax.plot(zAxisLine[0], zAxisLine[1], zAxisLine[2], 'r')ax.view_init(30,angle)# label the axesax.set_xlabel("PC1")ax.set_ylabel("PC2")ax.set_zlabel("PC3")ax.set_title("PCA Iris Dataset")filename='PCA/PCA_angle'+str(angle)+'.png'plt.savefig(filename, dpi=96)

def make_gif(input_folder, save_filepath):episode_frames = []time_per_step = 0.25for root, _, files in os.walk(input_folder):file_paths = [os.path.join(root, file) for file in files]#sorted by modified timefile_paths = sorted(file_paths, key=lambda x: os.path.getmtime(x))episode_frames = [imageio.imread(file_path) for file_path in file_paths if file_path.endswith('.png')]episode_frames = np.array(episode_frames)imageio.mimsave(save_filepath, episode_frames, duration=time_per_step)make_gif('./PCA/', './PCA/PCA.gif')

from celluloid import Camera
import numpy as np
import matplotlib.pyplot as pltfig, axes = plt.subplots(2)
camera = Camera(fig)
t = np.linspace(0, 5 * np.pi, 128, endpoint=False)
t2 = np.linspace(0, 1 * np.pi, 128, endpoint=False)
for i in t2:axes[0].plot(t, np.cos(t/2 + i), color='b')axes[1].plot(t2, np.sin(t2 - i), color='b')camera.snap()animation = camera.animate()
animation.save('celluloid_example.gif', writer = 'imagemagick')

import plotly.graph_objs as go
from plotly.offline import init_notebook_mode, iplot
init_notebook_mode(connected=True)
import plotly.plotly as py
import plotly
import pandas as pd
import datetimedf = pd.read_csv("TSLA.csv")df['date'] = df['date'].astype('str')
df['high'] = df['high'].astype('double')
df['low'] = df['low'].astype('double') date2 = []
for i in df['date']:new_date = datetime.datetime.strptime(i, "%d/%m/%Y").strftime("%Y-%m-%d")date2.append(new_date)df['date'] = df['date'].str.replace('/', '-')
df['date'] = date2
df.fillna(0)
df.head()

trace_high = go.Scatter(x=df.date,y=df['high'],name = "TSLA High",line = dict(color = '#17BECF'),opacity = 0.8)trace_low = go.Scatter(x=df.date,y=df['low'],name = "TSLA Low",line = dict(color = '#7F7F7F'),opacity = 0.8)data = [trace_high,trace_low]layout = dict(title='Time Series with Rangeslider',xaxis=dict(rangeselector=dict(buttons=list([dict(count=1,label='1m',step='month',stepmode='backward'),dict(count=6,label='6m',step='month',stepmode='backward'),dict(step='all')])),rangeslider=dict(visible = True),type='date')
)fig = dict(data=data, layout=layout)
iplot(fig, filename = "Time Series with Rangeslider")
plotly.offline.plot(fig, filename='Plotly_Stock.html')

import pandas as pd
import numpy as np
from bokeh.plotting import figure
from bokeh.io import output_file, show, output_notebook
from bokeh.models import CustomJS
from bokeh.models.widgets import CheckboxGroup
from bokeh.layouts import row
from bokeh.palettes import Viridis4
from bokeh.models.annotations import Title, Legenddf = pd.read_csv("TSLA.csv")df['date'] = pd.to_datetime(df['date'], format='%d/%m/%Y')p = figure(x_axis_type='datetime', plot_width=800)
aline = p.line(df['date'], df['high'], line_width=2, color=Viridis4[0])
bline = p.line(df['date'], df['low'], line_width=2, color=Viridis4[1])
cline = p.line(df['date'], df['open'], line_width=2, color=Viridis4[2])
dline = p.line(df['date'], df['close'], line_width=2, color=Viridis4[3])p.yaxis.axis_label = 'Price'
p.xaxis.axis_label = 'Time Span'legend = Legend(items=[("High Price",   [aline]),("Low Price", [bline]),("Open Price", [cline]),("Close Price", [dline])
], location=(0, 450))t = Title()
t.text = 'Tesla Stock Market Analysis'
p.title = tp.add_layout(legend, 'left')checkboxes = CheckboxGroup(labels=list(['High Price', 'Low Price', 'Open Price', 'Close Price']), active=[0, 1, 2, 3])
callback = CustomJS(code="""aline.visible = false; // aline and etc.. are bline.visible = false; // passed in from argscline.visible = false; dline.visible = false;// cb_obj is injected in thanks to the callbackif (cb_obj.active.includes(0)){aline.visible = true;} // 0 index box is alineif (cb_obj.active.includes(1)){bline.visible = true;} // 1 index box is blineif (cb_obj.active.includes(2)){cline.visible = true;} // 2 index box is cline etc...if (cb_obj.active.includes(3)){dline.visible = true;}""",args={'aline': aline, 'bline': bline, 'cline': cline, 'dline': dline})
checkboxes.js_on_click(callback)
output_file('Tesla_Stock_Widget.html')
show(row(p, checkboxes))

import seaborn as sns
sns.set(style="ticks")df = sns.load_dataset("iris")
sns.pairplot(df, hue="species")

from ipywidgets import interact
import numpy as np
import pandas as pd
from matplotlib import pyplot as plt
import nbinteract as nbidef cars(name, number):return '{} has {} cars!'.format(name, number)interact(cars, name='Nemo', number={'Zero': 0, 'Two': 2, 'Three': 3, 'Ten': 10});