from abupy import ABuSymbolPd
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
tsla_df = ABuSymbolPd.make_kl_df('usTSLA', n_folds=8)
tsla_df [['close', 'volume']].plot (subplots = True, style = ['r', 'g'],
grid = True)
print tsla_df [ ['close', 'volume']]
plt.show()
tsla_df.info()
tsla_df.describe(include = "all")
In above python code, I hope last code list the statistical of tsla_df, but it does not and also never give any error information. Anybody has any idea?
Related
I was wondering how one would create a 3D scatter chart in Taipy.
I tried this code initially:
import pandas as pd
import numpy as np
from taipy import Gui
df = pd.DataFrame(np.random.randint(0,100,size=(100, 3)), columns=list('xyz'))
df['cluster1']=np.random.randint(0,3,100)
my_page ="""
Creation of a 3-D chart:
<|{df}|chart|type=Scatter3D|x=x|y=y|z=z|mode=markers|color=cluster|>
"""
Gui(page=my_page).run()
This does indeed display a 3D plot, but the colors (clusters) do not show up.
Any hint?
Yes, you need some massaging of your dataframes to do it.
Here's a sample code that achieves this:
import pandas as pd
import numpy as np
from taipy import Gui
df = pd.DataFrame(np.random.randint(0,100,size=(100, 3)), columns=list('xyz'))
df['cluster1']=np.random.randint(0,3,100)
# Create a list of 3 dataframes, one per cluster
datas = [df[df['cluster1']==i] for i in range(3)]
properties = {
}
# create dynamically the property list.
# str(i) points to a dataframe index
# "/x" points to the column value in the selected dataframe
for i in range(len(datas)):
properties[f"x[{i+1}]"] = str(i)+"/x"
properties[f"y[{i+1}]"] = str(i)+"/y"
properties[f"z[{i+1}]"] = str(i)+"/z"
properties[f'name[{i+1}]'] = str(i+1)
print(properties)
chart = "<|{datas}|chart|type=Scatter3D|properties={properties}|mode=markers|height=800px|>"
Gui(page=chart).run()
In fact, with the new release: Taipy 1.1, this is very easy to do in a few lines of code:
import pandas as pd
import numpy as np
from taipy import Gui
color_map={0:"blue",1:'green', 2:"red"}
df = pd.DataFrame(np.random.randint(0,100,size=(100, 3)), columns=list('xyz'))
df['cluster1'] = np.random.randint(0,3,100)
df['cluster_colors'] = df.apply(lambda row: color_map[row.cluster1], axis=1)
marker = {"color":"cluster_colors"}
chart = "<|{df}|chart|type=Scatter3D|x=x|y=y|z=z|marker={marker}|mode=markers|height=800px|>"
Gui(page=chart).run()
If you want to leave it to Taipy to pick the colors for you, then you can simply use:
import pandas as pd
import numpy as np
from taipy import Gui
df = pd.DataFrame(np.random.randint(0,100,size=(100, 3)), columns=list('xyz'))
df['cluster1'] = np.random.randint(0,3,100)
marker = {"color":"cluster1"}
chart = "<|{df}|chart|type=Scatter3D|x=x|y=y|z=z|marker={marker}|mode=markers|height=800px|>"
Gui(page=chart).run()
I am already having Tkinter(someone said to install a tkinter)
code used:
imports are:
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
sns.set()
if u want to view the data-set then it is :
df = pd.read_csv("https://raw.githubusercontent.com/plotly/datasets/master/diabetes.csv")
code used to plot boxplot in jupyter notebook
fig, ax = plt.subplots(figsize = (20,20))
sns.boxplot(data = df,ax = ax)
)
I was supposed to add in my import's
%matplotlib inline
this is the code(I want to know the peak of the picture but I don't know how to add this kind of code)
import matplotlib.pyplot as plt
import matplotlib as mpl
import numpy as np
from scipy import stats
n=25
p=0.6
k=np.arange(0,50)
#the pmf forming
picture=stats.binom.pmf(k,n,p)
print(picture)
mpl.rcParams['font.sans-serif'] = [u'SimHei']
mpl.rcParams['axes.unicode_minus'] = False
mean,var,skew,kurt=stats.binom.stats(n,p,moments='mvsk')
print(mean,var,skew,kurt)
#the picture forming
plt.plot(k,picture,'o-')
plt.grid(True)
plt.show()
You can use scatter
import matplotlib.pyplot as plt
import matplotlib as mpl
import numpy as np
from scipy import stats
n=25
p=0.6
k=np.arange(0,50)
#the pmf forming
picture=stats.binom.pmf(k,n,p)
mpl.rcParams['font.sans-serif'] = [u'SimHei']
mpl.rcParams['axes.unicode_minus'] = False
mean,var,skew,kurt=stats.binom.stats(n,p,moments='mvsk')
print(mean,var,skew,kurt)
#the picture forming
plt.plot(k,picture,'o-')
plt.grid(True)
# the two new lines
max_ind = np.argmax(picture)
plt.scatter(x=k[max_ind],y=picture[max_ind],c='r',s=100,zorder=10)
and this produces
import pandas as pd
import numpy as np
import ipywidgets as widgets
from IPython.display import display
a = ['Banking', 'Auto', 'Life', 'Electric', 'Technology', 'Airlines',
'Healthcare']
df = pd.DataFrame(np.random.randn(7, 4), columns = list('ABCD'))
df.index = a
df.head(7)
dropdown = widgets.SelectMultiple(
options=df.index,
description='Sector',
disabled=False,
layout={'height':'100px', 'width':'40%'})
display(dropdown)
I want to create a function where I can filter the df by Sector. i.e say I select Airlines, Banking and Electric from the display(dropdown) and it returns a dataframe of the selected sectors only.
Try something like this, I have used a global variable to demonstrate in this case, but I would normally wrap up the functionality in a class so you always have access to the filtered dataframe.
Rather than use interact I have used .observe on the Selection widget.
import pandas as pd
import numpy as np
import ipywidgets as widgets
from IPython.display import display, clear_output
a = ['Banking', 'Auto', 'Life', 'Electric', 'Technology', 'Airlines',
'Healthcare']
df = pd.DataFrame(np.random.randn(7, 4), columns = list('ABCD'), index=a)
filtered_df = None
dropdown = widgets.SelectMultiple(
options=df.index,
description='Sector',
disabled=False,
layout={'height':'100px', 'width':'40%'})
def filter_dataframe(widget):
global filtered_df
selection = list(widget['new'])
with out:
clear_output()
display(df.loc[selection])
filtered_df = df.loc[selection]
out = widgets.Output()
dropdown.observe(filter_dataframe, names='value')
display(dropdown)
display(out)
Consider the MWE below. This will cause (Seaborn 0.7.1, Matplotlib 1.5.1):
/usr/local/lib/python3.5/dist-packages/matplotlib/tight_layout.py:222:
UserWarning: tight_layout : falling back to Agg renderer
warnings.warn("tight_layout : falling back to Agg renderer")
How to fix this?
MWE
import matplotlib
matplotlib.use('ps')
import pandas as pd
import random
import seaborn as sns
import matplotlib.pyplot as plt
ds = pd.DataFrame()
ds['x'] = random.sample(range(1, 100), 25)
ds['y'] = random.sample(range(1, 100), 25)
p = sns.jointplot(x = ds['x'],y = ds['y'],linewidth=1,edgecolor='black',alpha=0.3,stat_func=None)
plt.savefig("test.eps")