数据分析-Pandas练习题

参考网站：

https://github.com/fengdu78/machine_learning_beginner/tree/master/pandas/Pandas_Exercises

https://github.com/ajcr/100-pandas-puzzles

https://blog.csdn.net/jclian91/article/details/84289537

1. Pandas_Exercises

1.Getting and knowing

  • Chipotle
  • Occupation
  • World Food Facts

2.Filtering and Sorting

  • Chipotle
  • Euro12
  • Fictional Army

3.Grouping

  • Alcohol Consumption
  • Occupation
  • Regiment

4.Apply

  • Students
  • Alcohol Consumption
  • US_Crime_Rates

5.Merge

  • Auto_MPG
  • Fictitious Names
  • House Market

6.Stats

  • US_Baby_Names
  • Wind_Stats

7.Visualization

  • Chipotle
  • Titanic Disaster
  • Scores
  • Online Retail
  • Tips

8.Creating Series and DataFrames

  • Pokemon

9.Time Series

  • Apple_Stock
  • Getting_Financial_Data
  • Investor_Flow_of_Funds_US

10.Deleting

  • Iris
  • Wine

2. Pandas 100

#!/usr/bin/env python
# coding: utf-8

# In[1]:


import pandas as pd


# In[2]:


pd.__version__


# In[3]:


pd.show_versions()


# In[5]:


import numpy as np
data = {'animal': ['cat', 'cat', 'snake', 'dog', 'dog', 'cat', 'snake', 'cat', 'dog', 'dog'],
        'age': [2.5, 3, 0.5, np.nan, 5, 2, 4.5, np.nan, 7, 3],
        'visits': [1, 3, 2, 3, 2, 3, 1, 1, 2, 1],
        'priority': ['yes', 'yes', 'no', 'yes', 'no', 'no', 'no', 'yes', 'no', 'no']}

labels = ['a', 'b', 'c', 'd', 'e', 'f', 'g', 'h', 'i', 'j']


# In[52]:


df = pd.DataFrame(data, index=labels,dtype=float)


# In[53]:


df.info()


# In[8]:


df.describe() #对整型数据或浮点数进行求均值、四分位数


# In[12]:


#Return the first 3 rows of the DataFrame df.
df.iloc[:3] #返回三行数据，其余行的数据不返回


# In[13]:


df.head(5) #DataFrame由行和列组成，label为行标签


# In[14]:


df.loc[:, ['animal', 'age']]


# In[20]:


df.loc[:,['animal', 'priority']] #返回animal, priority列的所有数据


# In[21]:


#Select the data in rows [3, 4, 8] and in columns ['animal', 'age'].
df.loc[df.index[[3, 4, 8]], ['animal', 'age']]  #可见loc有时可以起到与iloc同样的作用


# In[23]:


#Select only the rows where the number of visits is greater than 3.
df[df['visits'] > 3]


# In[24]:


df[df['age'].isnull()] 


# In[25]:


df[(df['animal'] == 'cat') & (df['age'] < 3)]


# In[26]:


df[df['age'].between(2, 4)]


# In[27]:


df.loc['f', 'age'] = 1.5 #区分loc, iloc，loc[a, b],a为行属性，b为列属性


# In[28]:


df


# In[29]:


df['visits'].sum()


# In[30]:


df.groupby('animal')['age'].mean() #以animal分类，求age均值


# In[31]:


df.loc['k'] = [5.5, 'dog', 'no', 2] #loc增加k行


# In[32]:


df


# In[33]:


df = df.drop('k') #删除k行


# In[34]:


df['animal'].value_counts() #以animal分类，暂不明白，value_counts对何计数


# In[35]:


df.sort_values(by=['age', 'visits'], ascending=[False, True]) #按age降序,False降序，True升序，两者冲突时以前者为准


# In[38]:


df.sort_values(by = ['visits'],ascending = [False])


# In[39]:


#The 'priority' column contains the values 'yes' and 'no'. Replace this column with a column of boolean values: 'yes' should be True and 'no' should be False.
df['priority'] = df['priority'].map({'yes': True, 'no': False})
#map可以快速匹配字典的key，返回value


# In[40]:


df


# In[41]:


df['animal'] = df['animal'].replace('snake', 'python')


# In[42]:


df


# In[58]:


df.loc['d', 'age'] = 1
df.loc['h', 'age'] =5


# In[59]:


df


# In[62]:


df


# In[63]:


#For each animal type and each number of visits, find the mean age. In other words, each row is an animal, each column is a number of visits and the values are the mean ages (hint: use a pivot table).
df.pivot_table(index= ['animal'], columns= ['visits'], values=['age'], aggfunc=['mean'])
#暂时有问题,因age存在NaN，故先填充数据,此外发现还需要先制定数据类型


# In[67]:


df = pd.DataFrame({'A': [1, 2, 2, 3, 4, 5, 5, 5, 6, 7, 7]})


# In[68]:


df


# In[69]:


#How do you filter out rows which contain the same integer as the row immediately above?
df.loc[df['A'].shift() != df['A']]


# In[72]:


df = pd.DataFrame(np.random.random(size=(5, 3)))


# In[73]:


df


# In[83]:


df.sub(df.mean(axis=1), axis =0) #axis=1 对每行数据处理，输出每行，axis=0，对每列数据处理


# In[78]:


df.mean(axis = 1)


# In[84]:


df = pd.DataFrame(np.random.random(size=(5, 10)), columns=list('abcdefghij'))


# In[85]:


df


# In[86]:


df.sum().idxmin()


# In[87]:


#How do you count how many unique rows a DataFrame has (i.e. ignore all rows that are duplicates)
len(df.drop_duplicates(keep=False))


# In[108]:


df = pd.DataFrame(np.random.random(size=(6,10)), columns =list('abcdefghij') )


# In[109]:


df


# In[112]:


df.loc[4, 'g'] = np.nan
df.loc[1, 'd'] = np.nan
df.loc[5, 'f'] = np.nan


# In[113]:


df


# In[114]:


(df.isnull().cumsum(axis=1) == 3).idxmax(axis=1)


# In[115]:


df = pd.DataFrame({'grps': list('aaabbcaabcccbbc'), 
                   'vals': [12,345,3,1,45,14,4,52,54,23,235,21,57,3,87]})


# In[116]:


df


# In[117]:


df.groupby('grps')['vals'].nlargest(3).sum(level=0)


# In[143]:


df = pd.DataFrame({'A': range(1,101), 
                   'B': np.random.randint(1,100)})


# In[144]:


df


# df.groupby(pd.cut(df['A'], np.arange(0, 101, 10)))['B'].sum()

# In[146]:


df = pd.DataFrame({'X': [7, 2, 0, 3, 4, 2, 5, 0, 3, 4]})


# In[147]:


df


# In[149]:


x = (df['X'] != 0).cumsum()
y = x != x.shift()
df['Y'] = y.groupby((y != y.shift()).cumsum()).cumsum()


# df 截止到第29题

3. Pandas Tutorial

 

4. 函数用法积累

4.1 pd.Categorical

返回字符所属类别的位置索引

posted @ 2019-08-14 10:45  JaconHunt  阅读(1490)  评论(0)  编辑  收藏  举报