Python笔记 #08# NumPy: Statistic Basis

数据分析的基本步骤：

1. 了解你的数据（get to know your data），
2. 做一些统计学处理（像僵尸一样盯着数字不会带给你任何灵感！）
3. 实现可视化（get a better feeling for your data.）。

 

1、numpy 自带生成数据的函数

 

2、numpy 具有各种统计学函数

# np_baseball is available

# Import numpy
import numpy as np

# Create np_height from np_baseball
np_height = np_baseball[:,0]

# Print out the mean of np_height
print(np.mean(np_height))

# Print out the median of np_height
print(np.median(np_height))

/

# np_baseball is available

# Import numpy
import numpy as np

# Print mean height (first column)
avg = np.mean(np_baseball[:,0])
print("Average: " + str(avg))

# Print median height. Replace 'None'
med = np.median(np_baseball[:,0])
print("Median: " + str(med))

# Print out the standard deviation on height. Replace 'None'
stddev = np.std(np_baseball[:,0])
print("Standard Deviation: " + str(stddev))

# Print out correlation between first and second column. Replace 'None'
corr = np.corrcoef(np_baseball[:,0], np_baseball[:,1])
print("Correlation: " + str(corr))

 /

# heights and positions are available as lists

# Import numpy
import numpy as np

# Convert positions and heights to numpy arrays: np_positions, np_heights
np_positions = np.array(positions)
np_heights = np.array(heights)

# Heights of the goalkeepers: gk_heights
gk_heights = np_heights[np_positions == 'GK']

# Heights of the other players: other_heights
other_heights = np_heights[np_positions != 'GK']

# Print out the median height of goalkeepers. Replace 'None'
print("Median height of goalkeepers: " + str(np.median(gk_heights)))

# Print out the median height of other players. Replace 'None'
print("Median height of other players: " + str(np.median(other_heights)))

 

3、numpy 貌似不可以做数据可视化······

可视化是从数据中获取灵感、直觉的一种途经！

转载于:https://www.cnblogs.com/xkxf/p/8277301.html