Intermediate Python -- DataCamp
进阶数据操作(Dictionary&DataFrame)
List
数据单独列出来就是List
pop = [30.55, 2.77, 39.21]
countries = ["afghanistan", "albania", "algeria"]
ind_alb = countries.index("albania")
ind_alb
1
pop[ind_alb]
2.77
list使用起来不是很方便。
Dictionary
两组list的数据对应起来的话就变成Dictionary了
world = {"afghanistan":30.55, "albania":2.77, "algeria":39.21}
world["albania"]
2.77
- 新增或者删除数值
world["sealand"] = 0.000028
world
{'afghanistan': 30.55, 'albania': 2.81, 'algeria': 39.21, 'sealand': 2.8e-05}
del(world["sealand"]) world
{'afghanistan': 30.55, 'albania': 2.81, 'algeria': 39.21}
- 多层字典
说白了就是字典里面套字典
# Dictionary of dictionaries
europe = { 'spain': { 'capital':'madrid', 'population':46.77 },
'france': { 'capital':'paris', 'population':66.03 },
'germany': { 'capital':'berlin', 'population':80.62 },
'norway': { 'capital':'oslo', 'population':5.084 } }
# Print out the capital of France
europe["france"]["capital"]
# Create sub-dictionary data
data={"capital":"rome","population":59.83}
# Add data to europe under key 'italy'
europe["italy"]=data
# Print europe
print(europe)
{'france': {'population': 66.03, 'capital': 'paris'}, 'italy': {'population': 59.83, 'capital': 'rome'}, 'germany': {'population': 80.62, 'capital': 'berlin'}, 'norway': {'population': 5.084, 'capital': 'oslo'}, 'spain': {'population': 46.77, 'capital': 'madrid'}}
DataFrame
数据分析里用的最多的其实还是DataFrame(数据框)，操作数据框的话会用到pandas工具包 。
- 把字典转换成数据框
关键语法
import xx as x
pd.DataFrame()
# Pre-defined lists
names = ['United States', 'Australia', 'Japan', 'India', 'Russia', 'Morocco', 'Egypt']
dr = [True, False, False, False, True, True, True]
cpc = [809, 731, 588, 18, 200, 70, 45]
# Import pandas as pd
import pandas as pd
# Create dictionary my_dict with three key:value pairs: my_dict
my_dict={"country":names,"drives_right":dr,"cars_per_cap":cpc}
# Build a DataFrame cars from my_dict: cars
cars=pd.DataFrame(my_dict)
# Print cars
print(cars)
cars_per_cap country drives_right
0 809 United States True
1 731 Australia False
2 588 Japan False
3 18 India False
4 200 Russia True
5 70 Morocco True
6 45 Egypt True
- 设置行名
cars.index=xxxxx
import pandas as pd
# Build cars DataFrame
names = ['United States', 'Australia', 'Japan', 'India', 'Russia', 'Morocco', 'Egypt']
dr = [True, False, False, False, True, True, True]
cpc = [809, 731, 588, 18, 200, 70, 45]
cars_dict = { 'country':names, 'drives_right':dr, 'cars_per_cap':cpc }
cars = pd.DataFrame(cars_dict,)
print(cars)
# Definition of row_labels
row_labels = ['US', 'AUS', 'JPN', 'IN', 'RU', 'MOR', 'EG']
# Specify row labels of cars
cars.index=row_labels
# Print cars again
print(cars)
cars_per_cap country drives_right
0 809 United States True
1 731 Australia False
2 588 Japan False
3 18 India False
4 200 Russia True
5 70 Morocco True
6 45 Egypt True
cars_per_cap country drives_right
US 809 United States True
AUS 731 Australia False
JPN 588 Japan False
IN 18 India False
RU 200 Russia True
MOR 70 Morocco True
EG 45 Egypt True
- 读取csv文件
cars= pd.read_csv("cars.csv",index_col = 0)
Pandas的简单操作
之后会花篇幅详细学习Pandas
- 选择列
[]和[[ ]]的区别，带列名和不带列名
print(cars["country"])
US United States
AUS Australia
JPN Japan
IN India
RU Russia
MOR Morocco
EG Egypt
Name: country, dtype: object
print(cars[["country"]])
country
US United States
AUS Australia
JPN Japan
IN India
RU Russia
MOR Morocco
EG Egypt
print(cars[["country","drives_right"]])
country drives_right
US United States True
AUS Australia False
JPN Japan False
IN India False
RU Russia True
MOR Morocco True
EG Egypt True
- 选择行
可以选取指定行，这个和R很相似
# Import cars data
import pandas as pd
cars = pd.read_csv('cars.csv', index_col = 0)
# Print out first 3 observations
print(cars[0:3])
# Print out fourth, fifth and sixth observation
print(cars[3:6])
cars_per_cap country drives_right
US 809 United States True
AUS 731 Australia False
JPN 588 Japan False
cars_per_cap country drives_right
IN 18 India False
RU 200 Russia True
MOR 70 Morocco True
print(cars[0:3])
cars_per_cap country drives_right
US 809 United States True
AUS 731 Australia False
JPN 588 Japan False
<script.py> output:
cars_per_cap country drives_right
US 809 United States True
AUS 731 Australia False
JPN 588 Japan False
cars_per_cap country drives_right
IN 18 India False
RU 200 Russia True
MOR 70 Morocco True
loc and iloc
# Import cars data
import pandas as pd
cars = pd.read_csv('cars.csv', index_col = 0)
# Print out drives_right value of Morocco
print(cars.loc[["MOR","drives_right"]])
# Print sub-DataFrame
print(cars.loc[["RU","MOR"],["country","drives_right"]])
<script.py> output:
cars_per_cap country drives_right
MOR 70.0 Morocco True
drives_right NaN NaN NaN
country drives_right
RU Russia True
MOR Morocco True
and, or, not
看下面这个简单的例子就好,直接用and or
# Define variables
my_kitchen = 18.0
your_kitchen = 14.0
# my_kitchen bigger than 10 and smaller than 18?
print(my_kitchen > 10 and my_kitchen <10)
# my_kitchen smaller than 14 or bigger than 17?
print(my_kitchen<14 or my_kitchen>17)
# Double my_kitchen smaller than triple your_kitchen?
print(2*my_kitchen < 3*your_kitchen)
<script.py> output:
False
True
True
not也是直接用not
In [1]:
x = 8
y = 9
not(not(x < 3) and not(y > 14 or y > 10))
Out[1]:
False
用numpy来进行类似的操作
np.logical_and
np.logical_or
np.logical_not
# Create arrays
import numpy as np
my_house = np.array([18.0, 20.0, 10.75, 9.50])
your_house = np.array([14.0, 24.0, 14.25, 9.0])
# my_house greater than 18.5 or smaller than 10
print(np.logical_or(my_house >18.5,my_house<10))
# Both my_house and your_house smaller than 11
print(np.logical_and(my_house<11,your_house<11))
得到结果
<script.py> output:
[False True False True]
[False False False True]
if, elif, else
条件后面用的:分隔
area = 10.0
if(area < 9) :
print("small")
elif(area < 12) :
print("medium")
else :
print("large")
比较一下，R语言里用的是{}
if (test_expression) {
statement1
} else {
statement2
}
Python的简洁易懂的优势就体现出来了。
再举个例子, Python可以没有规矩到条件不需要加()
# Define variables
room = "kit"
area = 14.0
# if-else construct for room
if room == "kit" :
print("looking around in the kitchen.")
else :
print("looking around elsewhere.")
# if-else construct for area
if area > 15 :
print("big place!")
else :
print("pretty small.")
# if statement for room
if room == "kit" :
print("looking around in the kitchen.")
# if statement for area
if area>15 :
print("big place!")
练习题
Remember about np.logical_and(), np.logical_or() and np.logical_not(), the Numpy variants of the and, or and notoperators? You can also use them on Pandas Series to do more advanced filtering operations.
Take this example that selects the observations that have a cars_per_cap between 10 and 80. Try out these lines of code step by step to see what's happening.
cpc = cars['cars_per_cap']
between = np.logical_and(cpc > 10, cpc < 80)
medium = cars[between]
# Import cars data
import pandas as pd
cars = pd.read_csv('cars.csv', index_col = 0)
# Import numpy, you'll need this
import numpy as np
# Create medium: observations with cars_per_cap between 100 and 500
cpc = cars['cars_per_cap']
between = np.logical_and(cpc > 100, cpc < 500)
medium = cars[between]
# Print medium
print(medium)
output
<script.py> output:
cars_per_cap country drives_right
RU 200 Russia True
while
语法结构,用:隔开
while condition :
expression
x = 1
while x < 4 :
print(x)
x = x + 1
# Initialize offset
offset = 8
# Code the while loop
while offset != 0:
print("correcting...")
offset = offset-1
print(offset)
output
<script.py> output:
correcting...
7
correcting...
6
correcting...
5
correcting...
4
correcting...
3
correcting...
2
correcting...
1
correcting...
0
-
while和if的混搭
# Initialize offset
offset = -6
# Code the while loop
while offset != 0 :
print("correcting...")
if offset > 0 :
offset = offset -1
else :
offset = offset + 1
print(offset)
for循环
List里的for循环
# areas list
areas = [11.25, 18.0, 20.0, 10.75, 9.50]
# Code the for loop
for i in areas :
print(i)
-
enumerate, 可以同时输出变量的序列和数值
l = ['Alice', 'Bob', 'Charlie']
for name in l:
print(name)
# Alice
# Bob
# Charlie
for i, name in enumerate(l):
print(i, name)
# 0 Alice
# 1 Bob
# 2 Charlie
默认是从0开始，可以指定从1或者从任意数字开始。
for i, name in enumerate(l, 1):
print(i, name)
# 1 Alice
# 2 Bob
# 3 Charlie
自定义输出格式
# areas list
areas = [11.25, 18.0, 20.0, 10.75, 9.50]
# Code the for loop
for index, area in enumerate(areas) :
print("room " + str(index+1) + ": " + str(area))
output
room 1: 11.25
room 2: 18.0
room 3: 20.0
room 4: 10.75
room 5: 9.5
双层list的for循环也是毫不费力
# house list of lists
house = [["hallway", 11.25],
["kitchen", 18.0],
["living room", 20.0],
["bedroom", 10.75],
["bathroom", 9.50]]
# Build a for loop from scratch
for x, y in house:
print("the " + str(x) + " is " + str(y) + " sqm" )
the hallway is 11.25 sqm
the kitchen is 18.0 sqm
the living room is 20.0 sqm
the bedroom is 10.75 sqm
the bathroom is 9.5 sqm
Dictionary里的for循环
和list不一样的是需要用到items()这个指令
# Definition of dictionary
europe = {'spain':'madrid', 'france':'paris', 'germany':'berlin',
'norway':'oslo', 'italy':'rome', 'poland':'warsaw', 'austria':'vienna' }
# Iterate over europe
for k, v in europe.items() :
print("the capital of " + k + " is " + v)
Numpy array里的for循环
numpy array可以是1D也可以是2D，1D的话和list一样，2D的话要用到np.nditer,把两列按照一列来显示。
# Import numpy as np
import numpy as np
# For loop over np_height
for x in np_height :
print(str(x) + " inches")
# For loop over np_baseball
for val in np.nditer(np_baseball) :
print(val)
Dataframe里的for循环
iterrows()按照行来分析。
# Import cars data
import pandas as pd
cars = pd.read_csv('cars.csv', index_col = 0)
cars.head()
# Iterate over rows of cars
for lab, row in cars.iterrows() :
print(lab)
print(row)
output
cars.head()
cars_per_cap country drives_right
US 809 United States True
AUS 731 Australia False
JPN 588 Japan False
IN 18 India False
RU 200 Russia True
<script.py> output:
US
cars_per_cap 809
country United States
drives_right True
Name: US, dtype: object
AUS
cars_per_cap 731
country Australia
drives_right False
Name: AUS, dtype: object
JPN
cars_per_cap 588
country Japan
drives_right False
Name: JPN, dtype: object
IN
cars_per_cap 18
country India
drives_right False
Name: IN, dtype: object
RU
cars_per_cap 200
country Russia
drives_right True
Name: RU, dtype: object
MOR
cars_per_cap 70
country Morocco
drives_right True
Name: MOR, dtype: object
EG
cars_per_cap 45
country Egypt
drives_right True
Name: EG, dtype: object
两种方法添加列
# Import cars data
import pandas as pd
cars = pd.read_csv('cars.csv', index_col = 0)
cars.head()
# Code for loop that adds COUNTRY column
for lab, row in cars.iterrows():
cars.loc[lab,"COUNTRY"] = str.upper(row["country"])
# Print cars
print(cars)
2.apply
# Import cars data
import pandas as pd
cars = pd.read_csv('cars.csv', index_col = 0)
cars["country"]
# Use .apply(str.upper)
for lab, row in cars.iterrows() :
cars["COUNTRY"] = cars["country"].apply(str.upper)
print(cars)
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