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