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import numpy as np
from matplotlib import pyplot as plt
theta = np.linspace(0, 2*np.pi, 1000)
r = 3* np.sin(8 * theta)
plt.polar(theta, r, 'r')
plt.show()
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import numpy as np
from matplotlib import pyplot as plt
theta = np.linspace(0, 2*np.pi, 1000)
r = 3* np.sin(8 * theta)
plt.polar(theta+(r<0)*np.pi, np.abs(r), 'r')
plt.show()
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import numpy as np
x = np.linspace(0, 2.0)
y = np.sin(np.pi*x)**2
plt.plot(x, y);
import seaborn as sns
iris = sns.load_dataset('iris')
sns.violinplot(x=iris["species"], y=iris["sepal_length"])<AxesSubplot:xlabel='species', ylabel='sepal_length'>
Ten baseline variables, age, sex, body mass index, average blood pressure, and six blood serum measurements were obtained for each of n = 442 diabetes patients, as well as the response of interest, a quantitative measure of disease progression one year after baseline.
## Load diabetes datasetfrom sklearn.datasets import load_diabetes
import pandas as pd
diabeties = load_diabetes()
diabeties_df = pd.DataFrame(diabeties.data, columns=diabeties.feature_names)
diabeties_df.dataframe tbody tr th {
vertical-align: top;
}
.dataframe thead th {
text-align: right;
}
| age | sex | bmi | bp | s1 | s2 | s3 | s4 | s5 | s6 | |
|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 0.038076 | 0.050680 | 0.061696 | 0.021872 | -0.044223 | -0.034821 | -0.043401 | -0.002592 | 0.019908 | -0.017646 |
| 1 | -0.001882 | -0.044642 | -0.051474 | -0.026328 | -0.008449 | -0.019163 | 0.074412 | -0.039493 | -0.068330 | -0.092204 |
| 2 | 0.085299 | 0.050680 | 0.044451 | -0.005671 | -0.045599 | -0.034194 | -0.032356 | -0.002592 | 0.002864 | -0.025930 |
| 3 | -0.089063 | -0.044642 | -0.011595 | -0.036656 | 0.012191 | 0.024991 | -0.036038 | 0.034309 | 0.022692 | -0.009362 |
| 4 | 0.005383 | -0.044642 | -0.036385 | 0.021872 | 0.003935 | 0.015596 | 0.008142 | -0.002592 | -0.031991 | -0.046641 |
| ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... |
| 437 | 0.041708 | 0.050680 | 0.019662 | 0.059744 | -0.005697 | -0.002566 | -0.028674 | -0.002592 | 0.031193 | 0.007207 |
| 438 | -0.005515 | 0.050680 | -0.015906 | -0.067642 | 0.049341 | 0.079165 | -0.028674 | 0.034309 | -0.018118 | 0.044485 |
| 439 | 0.041708 | 0.050680 | -0.015906 | 0.017282 | -0.037344 | -0.013840 | -0.024993 | -0.011080 | -0.046879 | 0.015491 |
| 440 | -0.045472 | -0.044642 | 0.039062 | 0.001215 | 0.016318 | 0.015283 | -0.028674 | 0.026560 | 0.044528 | -0.025930 |
| 441 | -0.045472 | -0.044642 | -0.073030 | -0.081414 | 0.083740 | 0.027809 | 0.173816 | -0.039493 | -0.004220 | 0.003064 |
442 rows × 10 columns
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import seaborn as sns
sns.histplot(data=diabeties_df, x='bmi');
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