pandas apply lambda_Pandas 练习
学过python，并且希望从事数据分析的都应该知道，pandas是一个经常用到的库，所以对这个库的学习就比较重要的。找了很久终于找到了一个比较合适的练习题，这个练习题是Github上面的，地址在这guipsamora/pandas_exercisesgithub.com有兴趣的可以去试着做一下，就像作者说的，to learn is to do. So unless you practice yo
学过python，并且希望从事数据分析的都应该知道，pandas是一个经常用到的库，所以对这个库的学习就比较重要的。
找了很久终于找到了一个比较合适的练习题，这个练习题是Github上面的，地址在这
guipsamora/pandas_exercisesgithub.com
有兴趣的可以去试着做一下，就像作者说的，to learn is to do. So unless you practice you won't learn.
总共有11个练习章节，由浅入深，刚好可以通过这个练习来检验一下自己的pandas水平
欧克，让我们开始吧
第一个，Getting&Knowing Your Data ----理解数据之Chipo
由于这个章节的题都很基础，我就一带而过，不具体展开了Ex2 - Getting and Knowing your Data
This time we are going to pull data directly from the internet. Special thanks to: https://github.com/justmarkham for sharing the dataset and materials.Step 1. Import the necessary libraries
In Step 2. Import the dataset from this address.
Step 3. Assign it to a variable called chipo.
In [ ]:
#这两题也是很简单的，从给出的地址导入数据
url = 'https://raw.githubusercontent.com/justmarkham/DAT8/master/data/chipotle.tsv'
chipo = pd.read_csv(url, sep = 't')
第四题
Step 4. See the first 10 entries
chipo.head(10)#output
第五题
#这个题也很简单，让求这个数据集的数据量，要求给出两种方法
Step 5. What is the number of observations in the dataset?
In [1]:chipo.shape[0]
# Solution 1
In [2]:chipo.info()
# Solution 2
#上面两个语句都是可以求得数据量的Step 6. What is the number of columns in the dataset?
#这个题和第五题考的内容基本一致
chipo.shape[1]Step 7. Print the name of all the columns.
chipo.columnsStep 8. How is the dataset indexed?
In [ ]:#考数据集的索引的
chipo.indexStep 9. Which was the most-ordered item?
In [ ]:
#这个是考订购最多的item
c = chipo.groupby('item_name')
c.max().head(1)
Step 10. For the most-ordered item, how many items were ordered?
In [ ]:#让在第九题的基础上，求出有多少item被订购了
c = chipo.groupby('item_name')
c = c.sum()
c = c.sort_values(['quantity'], ascending=False)
c.head(1)第11题Step 11. What was the most ordered item in the choice_description column?
In [ ]:
c = chipo.groupby('choice_description').sum()
c = c.sort_values(['quantity'], ascending=False)
c.head(1)
Step 12. How many items were orderd in total?
In [ ]:
#求和
total_items_ordered = chipo['quantity'].sum()
total_items_orderedStep 13. Turn the item price into a float
#首先看一下price这一列是什么类型的数据，Step 13.a. Check the item price type
In [ ]:chipo['item_price'].dtype
Step 13.b. Create a lambda function and change the type of item price
In [ ]:#原题让使用一个lambda函数，来转换数据类型
func = lambda x: float(x[1:-1])#我这里使用了[1:-1]代表了全部的数据，
chipo.item_price = chipo.item_price.apply(func)Step 13.c. Check the item price type
In [ ]:
chipo.item_price.dtype#其实这里还有另外一种解法
chipo.item_price = pd.to_numeric(chipo.item_price,
downcast='float')
chipo.item_price.dtype
#引用to_numeric这个函数，可以直接对整列数据进行转换。Step 14. How much was the revenue for the period in the dataset?
In [ ]:#revenue是收益的意思
#revenue=quantity*price
revenue = (chipo['quantity'] * chipo['item_price']).sum()
print('Revenue was :$',str(revenue))
Step 15. How many orders were made in the period?
In [ ]:
orders = chipo.order_id.value_counts().count()
ordersStep 16. What is the average revenue amount per order?
In [3]:
# Solution 1
#首先要求出总的revenue
revenue = (chipo['quantity'] * chipo['item_price']).sum()
#和总的orders
orders = chipo.order_id.value_counts().count()
avg = revenue/orders
avg
In [4]:
# Solution 2
#可以直接使用groupby函数
chipo.groupby(by=['order_id']).sum().mean()['revenue']
#先按照id分组，然后求和在求平均，然后取出revenue列Step 17. How many different items are sold?
In [ ]:
chipo.item_name.value_counts().count()
到这里第一章节的第一部分，就结束了
继续下一部分
Occupation
Ex3 - Getting and Knowing your Data
This time we are going to pull data directly from the internet. Special thanks to: https://github.com/justmarkham for sharing the dataset and materials.
Step 1. Import the necessary libraries
In [ ]:
Step 2. Import the dataset from this address.
url = ('https://raw.githubusercontent.com/justmarkham/DAT8/master/data/u.user')
users = pd.read_csv(url,sep='|')Step 3. Assign it to a variable called users and use the 'user_id' as index
In [ ]:url = ('https://raw.githubusercontent.com/justmarkham/DAT8/master/data/u.user')
users = pd.read_csv(url,sep='|',index_col='user_id')
Step 4. See the first 25 entries
In [ ]:users.head(25)
Step 5. See the last 10 entries
In [ ]:
#tail是从后往前显示的
users.tail(10)Step 6. What is the number of observations in the dataset?
In [ ]:users.shape[0]
Step 7. What is the number of columns in the dataset?
In [ ]:
users.shape[1]Step 8. Print the name of all the columns.
In [ ]:print(users.columns)
Step 9. How is the dataset indexed?
In [ ]:users.index
Step 10. What is the data type of each column?
In [ ]:
users.dtypesStep 11. Print only the occupation column
In [ ]:users.occupation
#or
users['occupation']Step 12. How many different occupations there are in this dataset?
In [ ]:users.occupation.nunique()
Step 13. What is the most frequent occupation?
In [ ]:
users.occupation.value_counts().head(1)Step 14. Summarize the DataFrame.
In [ ]:
users.describe()Step 15. Summarize all the columns
In [ ]:
#因为describe默认的是只对数字型起作用
users.describe(include='all')Step 16. Summarize only the occupation column
In [ ]: users.occupation.describe()
Step 17. What is the mean age of users?
In [ ]:
users.age.mean()Step 18. What is the age with least occurrence?
In [ ]:
users.age.value_counts().tail()小总结：基础的了解数据集的过程，是很程序化的，基本就是这上面提到的一些语句，head，tail，count，describe，mean,index，columns,info,nuique等等吧，主要使用这些语句来一窥数据集，了解数据集，获取数据集。
下面开始第二部分，分组和聚合数据
Filtering and Sorting Data
This time we are going to pull data directly from the internet. Special thanks to: https://github.com/justmarkham for sharing the dataset and materials.
Ex1 - Filtering and Sorting Data
This time we are going to pull data directly from the internet. Special thanks to: https://github.com/justmarkham for sharing the dataset and materials.
Step 1. Import the necessary libraries
In [ ]:
Step 2. Import the dataset from this address.
Step 3. Assign it to a variable called chipo.
In [ ]:
这里之前已经做过，不在重复
¶
In [ ]:
Step 5. Sort by the name of the item
In [ ]:
chipo.item_name.sort_values()
#or
chipo.sort_values(by='item_name')['item_name']Step 6. What was the quantity of the most expensive item ordered?
In [ ]:
chipo.sort_values(by = "item_price", ascending = False).head(1)Step 7. How many times were a Veggie Salad Bowl ordered?
In [ ]:chipo_salad = chipo[chipo.item_name == "Veggie Salad Bowl"]
len(chipo_salad)
Step 8. How many times people orderd more than one Canned Soda?
In [ ]:
chipo_drink_steak_bowl = chipo[(chipo.item_name == "Canned Soda") & (chipo.quantity > 1)]
len(chipo_drink_steak_bowl)敲了一天的代码，就到此为止吧！
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