plot with hist function get wrong normalized probability density figure - python-3.x

I want drow a simple hist plot, with density=True， the data saved in the data.txt file, I put here:
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1.361864184454894544e-02
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7.929557208005727498e-03
3.413561032196532619e-03
enter image description herejust got wrong figure[enter image
description here][2], using python 3.7.
import matplotlib.pyplot as plt
import pandas as pd
data = pd.read_csv('data.txt', header=-1)
data.columns =['A']
data.hist('A', bins=20, density=True)
plt.show()

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And I want to create a seaborn heatmap like this:
plt.figure(figsize=(470, 60))
sns.set(font_scale = 14)
df=comparison.T
# create a Boolean mask of df
mask = df.ge(1).all()
# use the mask to update a list of labels
cols = [col if m else '' for (col, m) in zip(df.columns, mask)]
# plot with custom labels
ax = sns.heatmap(df, xticklabels=cols,cmap="crest_r")
ax.set_xticklabels(labels=cols, fontsize=200)
plt.show()
However, sometimes due to the narrow space the xtick labels overlap. Is there any way to add more spacing while still providing a readable image (not too small so that it cannot be read) or to put them one below the other?

seaborn not plotting scatterplot as expected

I have written the following simple code :
import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt
advertising = pd.read_csv("tv-marketing.csv")
sns.pairplot(advertising, x_vars=[
'TV'], y_vars='Sales', height=7, aspect=0.7)
The following is the .csv file that is being used here -
TV,Sales
230.1,22.1
44.5,10.4
17.2,9.3
151.5,18.5
180.8,12.9
8.7,7.2
57.5,11.8
120.2,13.2
8.6,4.8
199.8,10.6
66.1,8.6
214.7,17.4
23.8,9.2
97.5,9.7
204.1,19
195.4,22.4
67.8,12.5
281.4,24.4
69.2,11.3
147.3,14.6
218.4,18
237.4,12.5
13.2,5.6
228.3,15.5
62.3,9.7
262.9,12
142.9,15
240.1,15.9
248.8,18.9
70.6,10.5
292.9,21.4
112.9,11.9
97.2,9.6
265.6,17.4
95.7,9.5
290.7,12.8
266.9,25.4
74.7,14.7
43.1,10.1
228,21.5
202.5,16.6
177,17.1
293.6,20.7
206.9,12.9
25.1,8.5
175.1,14.9
89.7,10.6
239.9,23.2
227.2,14.8
66.9,9.7
199.8,11.4
100.4,10.7
216.4,22.6
182.6,21.2
262.7,20.2
198.9,23.7
7.3,5.5
136.2,13.2
210.8,23.8
210.7,18.4
53.5,8.1
261.3,24.2
239.3,15.7
102.7,14
131.1,18
69,9.3
31.5,9.5
139.3,13.4
237.4,18.9
216.8,22.3
199.1,18.3
109.8,12.4
26.8,8.8
129.4,11
213.4,17
16.9,8.7
27.5,6.9
120.5,14.2
5.4,5.3
116,11
76.4,11.8
239.8,12.3
75.3,11.3
68.4,13.6
213.5,21.7
193.2,15.2
76.3,12
110.7,16
88.3,12.9
109.8,16.7
134.3,11.2
28.6,7.3
217.7,19.4
250.9,22.2
107.4,11.5
163.3,16.9
197.6,11.7
184.9,15.5
289.7,25.4
135.2,17.2
222.4,11.7
296.4,23.8
280.2,14.8
187.9,14.7
238.2,20.7
137.9,19.2
25,7.2
90.4,8.7
13.1,5.3
255.4,19.8
225.8,13.4
241.7,21.8
175.7,14.1
209.6,15.9
78.2,14.6
75.1,12.6
139.2,12.2
76.4,9.4
125.7,15.9
19.4,6.6
141.3,15.5
18.8,7
224,11.6
123.1,15.2
229.5,19.7
87.2,10.6
7.8,6.6
80.2,8.8
220.3,24.7
59.6,9.7
0.7,1.6
265.2,12.7
8.4,5.7
219.8,19.6
36.9,10.8
48.3,11.6
25.6,9.5
273.7,20.8
43,9.6
184.9,20.7
73.4,10.9
193.7,19.2
220.5,20.1
104.6,10.4
96.2,11.4
140.3,10.3
240.1,13.2
243.2,25.4
38,10.9
44.7,10.1
280.7,16.1
121,11.6
197.6,16.6
171.3,19
187.8,15.6
4.1,3.2
93.9,15.3
149.8,10.1
11.7,7.3
131.7,12.9
172.5,14.4
85.7,13.3
188.4,14.9
163.5,18
117.2,11.9
234.5,11.9
17.9,8
206.8,12.2
215.4,17.1
284.3,15
50,8.4
164.5,14.5
19.6,7.6
168.4,11.7
222.4,11.5
276.9,27
248.4,20.2
170.2,11.7
276.7,11.8
165.6,12.6
156.6,10.5
218.5,12.2
56.2,8.7
287.6,26.2
253.8,17.6
205,22.6
139.5,10.3
191.1,17.3
286,15.9
18.7,6.7
39.5,10.8
75.5,9.9
17.2,5.9
166.8,19.6
149.7,17.3
38.2,7.6
94.2,9.7
177,12.8
283.6,25.5
232.1,13.4
This is the output graph that I am getting (Running it from vscode->Run Current File
in Interactive Window
vscode output
But the expected output form the example that I took should be like this :
expected output 1
expected output 2
**Neither do I see the scatterplot, also the scaling is different.**

I believe the code you are looking for is the following:
sns.scatterplot(x='TV', y='Sales', data=advertising)
If you want to use pairplot then you can use:
sns.pairplot(advertising, height=7, aspect=0.7)
or:
sns.pairplot(sample_data, x_vars=['TV'], y_vars=['Sales'], height=7, aspect=0.7, kind='scatter', diag_kind=None)

Altair - Gradient above line

I want to create an area chart, however the gradient should run from the line up to the top of the chart. Any ideas?
example of a regular gradient chart here https://altair-viz.github.io/gallery/area_chart_gradient.html
alt.Chart(source).transform_filter(
'datum.symbol==="GOOG"'
).mark_area(
line={'color':'darkgreen'},
color=alt.Gradient(
gradient='linear',
stops=[alt.GradientStop(color='white', offset=0),
alt.GradientStop(color='darkgreen', offset=1)],
x1=1,
x2=1,
y1=1,
y2=0
)
).encode(
alt.X('date:T'),
alt.Y('price:Q')
)

You can do this by setting the y2 encoding to alt.value(0) – the zero in this case measures pixels from the top of the chart axis:
import altair as alt
from vega_datasets import data
source = data.stocks()
alt.Chart(source).transform_filter(
'datum.symbol==="GOOG"'
).mark_area(
line={'color':'darkgreen'},
color=alt.Gradient(
gradient='linear',
stops=[alt.GradientStop(color='white', offset=0),
alt.GradientStop(color='darkgreen', offset=1)],
x1=1,
x2=1,
y1=1,
y2=0
)
).encode(
alt.X('date:T'),
alt.Y('price:Q'),
y2=alt.value(0)
)

Get slope and itercept from a matched linear regression model in scikit-learn

I have a simple model
from sklearn import linear_model
x =[6.1101, 5.5277, 8.5186, 7.0032, 5.8598, 8.3829, 7.4764, 8.5781, 6.4862, 5.0546, 5.7107, 14.164, 5.734, 8.4084, 5.6407, 5.3794, 6.3654, 5.1301, 6.4296, 7.0708, 6.1891, 20.27, 5.4901, 6.3261, 5.5649, 18.945, 12.828, 10.957, 13.176, 22.203, 5.2524, 6.5894, 9.2482, 5.8918, 8.2111, 7.9334, 8.0959, 5.6063, 12.836, 6.3534, 5.4069, 6.8825, 11.708, 5.7737, 7.8247, 7.0931, 5.0702, 5.8014, 11.7, 5.5416, 7.5402, 5.3077, 7.4239, 7.6031, 6.3328, 6.3589, 6.2742, 5.6397, 9.3102, 9.4536, 8.8254, 5.1793, 21.279, 14.908, 18.959, 7.2182, 8.2951, 10.236, 5.4994, 20.341, 10.136, 7.3345, 6.0062, 7.2259, 5.0269, 6.5479, 7.5386, 5.0365, 10.274, 5.1077, 5.7292, 5.1884, 6.3557, 9.7687, 6.5159, 8.5172, 9.1802, 6.002, 5.5204, 5.0594, 5.7077, 7.6366, 5.8707, 5.3054, 8.2934, 13.394, 5.4369]
y = [17.592, 9.1302, 13.662, 11.854, 6.8233, 11.886, 4.3483, 12, 6.5987, 3.8166, 3.2522, 15.505, 3.1551, 7.2258, 0.71618, 3.5129, 5.3048, 0.56077, 3.6518, 5.3893, 3.1386, 21.767, 4.263, 5.1875, 3.0825, 22.638, 13.501, 7.0467, 14.692, 24.147, -1.22, 5.9966, 12.134, 1.8495, 6.5426, 4.5623, 4.1164, 3.3928, 10.117, 5.4974, 0.55657, 3.9115, 5.3854, 2.4406, 6.7318, 1.0463, 5.1337, 1.844, 8.0043, 1.0179, 6.7504, 1.8396, 4.2885, 4.9981, 1.4233, -1.4211, 2.4756, 4.6042, 3.9624, 5.4141, 5.1694, -0.74279, 17.929, 12.054, 17.054, 4.8852, 5.7442, 7.7754, 1.0173, 20.992, 6.6799, 4.0259, 1.2784, 3.3411, -2.6807, 0.29678, 3.8845, 5.7014, 6.7526, 2.0576, 0.47953, 0.20421, 0.67861, 7.5435, 5.3436, 4.2415, 6.7981, 0.92695, 0.152, 2.8214, 1.8451, 4.2959, 7.2029, 1.9869, 0.14454, 9.0551, 0.61705]
# Create linear regression object
regr = linear_model.LinearRegression()
# Train the model using the training sets
regr.fit([x], [y])
#where x and y are arrays of values
I need to get the slope and intercept. I tried regr.intercept_ but it returns a large array of numbers that I do not understand what it is.

You should do something like this to get the correct results:
import numpy as np
regr.fit(np.array(x).reshape(-1, 1), np.array(y).reshape(-1, 1))

What is the proper way to employ date2num for timestamps using candlestick_ohlc

My data looks like this (Date, Open, High, Low, Close):
ohlc = [
[1502929058, 1.2652, 1.2653, 1.265, 1.2653],
[1502929059, 1.267, 1.267, 1.267, 1.267],
[1502929060, 1.2655, 1.2656, 1.2655, 1.2656],
[1502929061, 1.2652, 1.2653, 1.2652, 1.2653],
[1502929062, 1.2631, 1.2631, 1.263, 1.2631],
[1502929063, 1.2625, 1.2625, 1.2625, 1.2625],
[1502929064, 1.2619, 1.2619, 1.2619, 1.2619],
[1502929065, 1.2622, 1.2623, 1.2622, 1.2623],
[1502929066, 1.2622, 1.2623, 1.2622, 1.2623],
[1502929067, 1.2617, 1.262, 1.2617, 1.262]
]
and I'm using the code blow to plot the candlesticks:
for row in ohlc:
row[0] = mdates.date2num(datetime.datetime.fromtimestamp(row[0]))
fig = plt.figure()
ax1 = plt.subplot2grid((1,1), (0,0))
candlestick_ohlc(ax1,ohlc,width=0.1)
fig.subplots_adjust(bottom=0.3)
ax1.xaxis.set_major_formatter(mdates.DateFormatter('%y-%m-%d %H:%M:%S'))
for label in ax1.xaxis.get_ticklabels():
label.set_rotation(45)
plt.xlabel('Date')
plt.ylabel('Price')
plt.show()
but the candlesticks being drawn on top of each other:
as I checked the code further, I noticed that mdates.date2num(datetime.datetime.fromtimestamp(row[0])) is actually generating dates with very minute differences (and therefore candlesticks being drawn on top of each other):
736558.1997453704
736558.1997569444
736558.1997685186
736558.1997800926
736558.1997916667
736558.1998032407
736558.1998148148
736558.1998263889
736558.199837963
736558.199849537
what is the solution to this problem?

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