课程笔记来源于是tiger的解密大数据课程。

python数据分析范式：

1. 安装anaconda

2. 安装 jupyter notebook

3. 切换路径为当数据文件所在路径

4. 启动jupyter notebook & 加载python包

5. 导入数据

6. 数据处理

7. 数据可视化

课上作业：

# coding: utf-8

# In[27]:

import numpy as np

import pandas as pd

import matplotlib.pyplot as plt

# In[38]:

get_ipython().magic(u'matplotlib inline')

get_ipython().magic(u"config InlineBackend.figure_format = 'retina'")

# In[29]:

iq_data = pd.read_csv('IQscore.csv')

iq_data.head(3)

# In[30]:

len(iq_data)

# In[31]:

iq=iq_data['IQ']

mean=iq.mean()

mean

# In[32]:

std = iq.std()

std

# In[33]:

def normfun(x,mu, sigma):

pdf = np.exp(-((x - mu)**2) / (2* sigma**2)) / (sigma * np.sqrt(2*np.pi))

return pdf

# In[34]:

x = np.arange(60, 150,1)

y = normfun(x, mean, std)

plt.plot(x,y, color='g',linewidth = 3)

plt.hist(iq, bins =7, color = 'r',alpha=0.5,rwidth= 0.9, normed=True)

plt.title('IQ distribution')

plt.xlabel('IQ score')

plt.ylabel('Probability')

plt.show()

课下作业

# coding: utf-8

# In[1]:

import numpy as np

import pandas as pd

import matplotlib.pyplot as plt

# In[3]:

get_ipython().magic(u'matplotlib inline')

get_ipython().magic(u"config InlineBackend.figure_format='retina'")

# In[4]:

stakes_data=pd.read_csv('stakes.csv')

# In[5]:

stakes_data.head(3)

# In[7]:

len(stakes_data)

# In[10]:

stakes=stakes_data['time']

mean=stakes.mean()

mean

# In[11]:

std=stakes.std()

std

# In[12]:

def normfun(x,mu,sigema):

pdf=np.exp(-(x-mu)**2/(2*sigema**2))/(sigema*np.sqrt(2*np.pi))

return pdf

# In[21]:

x=np.arange(145,155,0.2)

y=normfun(x,mean,std)

plt.plot(x,y,color='g',linewidth=3)

plt.hist(stakes,bins=10,color='r',alpha=0.5,rwidth=0.9,normed=True)

plt.title('stakes distribution')

plt.xlabel('time')

plt.ylabel('probability')

plt.show()

# In[ ]: