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Given the following text description, write Python code to implement the functionality described below step by step
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Description:
 IPython
Python
Step1:  Markdown
link
- ~~1~~
- 2
 - a
 - b
- 3  
multiline
    text
NumPy
Step2:  Pandas
Step3:  Matplotlib
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Python Code:
a = 1 + 2
a
a + 1
_
? sum
! ps -xa | grep python
import time
%time time.sleep(1)
Explanation: IPython
Python
End of explanation
import numpy as np
np.array([[1,2,3], [7,1,2]])
data = np.array([1,2,3,4,5])
data
data[1:-2]
data + 1
data * 2
data * data
np.sum(data * data)
data.dot(data)
data > 2
data[data > 2]
replaces = data[:]
replaces[data > 2] = -1
replaces
matrix = np.random.rand(5, 4)
matrix
matrix[1]
matrix[:, 1]
matrix[(1, 2), :]
import random
vec_len = 1000
v1 = [random.random() for _ in range(vec_len)]
v2 = [random.random() for _ in range(vec_len)]
%timeit [a * b for a, b in zip(v1, v2)]
np_v1, np_v2 = np.array(v1), np.array(v2)
%timeit np_v1 * np_v2
np.arange(1, 5, 0.7)
np.linspace(1, 5, 13)
first = np.array([[1, 2], [3, 4]])
second = np.array([[10, 20], [30, 40]])
np.hstack((first, second))
np.vstack((first, second))
Explanation: Markdown
link
- ~~1~~
- 2
 - a
 - b
- 3  
multiline
    text
NumPy
End of explanation
import pandas as pd
df = pd.read_csv('iris.csv')
df.head(3)
df[['Sepal.length', 'Petal.length']].head(5)
df[df['Sepal.length'] > 7.5]
df.describe()
%pylab inline
df['Sepal.length'].plot(kind='hist');
Explanation: Pandas
End of explanation
import matplotlib.pyplot as plt
x = np.arange(1, 10, 0.2)
y = np.sin(x)
plt.plot(x, y);
plt.scatter(x, y);
plt.plot(x, y, marker='o');
Explanation: Matplotlib
End of explanation
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