DataQuest上面的免费课程（本文是Python基础课程部分），里面有些很基础的东西（csv文件读，字符串预处理等），发在这里做记录。涉及下面六个案例：
- Find the lowest crime rate（读取csv文件，字符串切分，for循环和if判断过滤数据）
- Discover weather pattern in LA（for循环和if判断进行频数统计）
- Building a Spell Checker（词频统计，字符串预处理，字典跑字符串，统计正确错误单词）
- Analyze NFL data（使用CSVmodule导入文件，类，函数，使用字典和list进行简单统计）
- What should you name your kid if you want them to be a US Congressperson?（数据预处理，强制类型转换int()，try-except语句，字典方式统计，转存需要数据）
- Which airline is delayed the most?
- 附录：逐行读取txt文件
案例1 Find the lowest crime rate
（读取csv文件，字符串切分，for循环和if判断过滤数据）
crime_rates.csv是单sheet，73Rows，2Cols的文件。第一列是城市名称（字符串），第二列是犯罪数量（整数）。但是读入Python开始都是字符串，在后面类型转换将字符串形式的犯罪数量强制转换成整型。 并将分隔开转换后的数据存到full_data这个list中，然后使用for循环将犯罪数量最小的城市找出来（if判断，已知犯罪数最小为130），并将这个城市名存入变量city中。
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# We know that the lowest crime rate is 130.
# This is the second column of the data.
# We need to find the corresponding value in the first column -- the city with the lowest crime rate.
# Let‘s load the csv file
f = open(‘crime_rates.csv‘, ‘r‘)
data = f.read()
rows = data.split(‘\n‘)
full_data = []
for row in rows:
split_row = row.split(",")
split_row[1] = int(split_row[1])
full_data.append(split_row)
city = ""
lowest_crime_rate = 10000
for item in full_data:
if item[1] == 130:
city = item[0]
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案例2 Discover weather pattern in LA
（for循环和if判断进行频数统计）
两列数据的文本文件，有表头。导入la_weather.txt文本文件，切分，存入变量weather_data中，去掉表头。使用字典（dictionary）进行不同类型的频数统计。
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weather_data = []
f = open("la_weather.csv", ‘r‘)
data = f.read()
rows = data.split(‘\n‘)
for row in rows:
split_row = row.split(",")
weather_data.append(split_row)
print(weather_data)
#去掉表头
weather = weather_data[1:367]
weather_counts = {}
for item in weather:
if item in weather_counts:
weather_counts[item] = weather_counts[item] + 1
else:
weather_counts[item] = 1
print(weather_counts)
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案例3 Building a Spell Checker
（词频统计，字符串预处理，字典跑字符串，统计正确错误单词）
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# 将字符正规化，对字符进行处理，去掉特殊符号
def normalize(token):
token = token.replace(".","")
token = token.replace(",","")
token = token.replace("‘", "")
token = token.replace(";", "")
token = token.replace("\n", "")
token = token.lower()
return token
# 建立一个list用于存放正规的字典
normalized_dictionary_tokens = []
# 只读方式打开一个文件
f = open("dictionary.txt", "r")
raw_data = f.read()
# 按照空格将字符串进行切分，成单个单词
data = raw_data.split(" ")
# 遍历切分后的单词，进行正规化处理（def normalize，去掉特殊符号）
for token in data:
normalized_dictionary_tokens.append(normalize(token))
print(normalized_dictionary_tokens)
#统计正确单词和错误单词的词频。用一个正确单词的字典来遍历这个字符串，并进行统计
potential_misspellings = []
correctly_spelled = []
for token in normalized_story_tokens:
if token in normalized_dictionary_tokens:
correctly_spelled.append(token)
else:
potential_misspellings.append(token)
print(correctly_spelled)
print(potential_misspellings)
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案例4 Analyze NFL data
（使用CSVmodule导入文件，类，函数，使用字典和list进行简单统计）
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import csv
class Team():
def __init__(self, name):
self.name = name
f = open("nfl.csv", ‘r‘)
csvreader = csv.reader(f)
self.nfl = list(csvreader)
def count_total_wins(self):
count = 0
for row in self.nfl:
if row[2] == self.name:
count = count + 1
return count
def wins_by_years(self):
wins = {}
years = ["2009", "2010", "2011", "2012", "2013"]
for year in years:
count = 0
for row in self.nfl:
if row[2] == self.name and row[0] == year:
count += 1
wins[year] = count
return wins
niners = Team("San Francisco 49ers")
niners_wins_by_year = niners.wins_by_years()
print("Niners_wins_by_year: ", niners_wins_by_year)
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案例5 What should you name your kid if you want them to be a US Congressperson?
（数据预处理，强制类型转换int()，try-except语句，字典方式统计，转存需要数据）
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# legislators变量是一个2维list，大list里的其中一个list（条目）是一个有7个元素组成的（姓，名，出生年月日，未知，未知，未知）。我们要做的是这一组数据进行预处理，然后进行姓名的统计。
genders_list = []
unique_genders = set()
unique_genders_list = []
# 将性别数据以append方式挨个读入list变量genders_list中去
for row in legislators:
genders_list.append(row[3])
# genders_list变量使用set()函数进行元素去重变为字典，并存入字典变量unique_genders中，将去重后的结果再存储成list类型数据搭配到变量unique_genders_list
unique_genders = set(genders_list)
unique_genders_list = list(unique_genders)
print(genders_list)
# 已知性别数据的错误值为""，将其重赋值为“M”
for row in legislators:
if row[3] == "":
row[3] = "M"
# 统计出生年份存入list变量birth_years中。其中需要使用split方法对list中的某个元素进行切分，取其中第一个元素（即年），以append追加的方法存入list变量birth_years中
birth_years = []
for row in legislators:
birth_list = []
birth_list = row[2].split("-")
birth_years.append(birth_list[0])
# 对list变量进行enumerate()函数操作（得到下标和所在的当前row）类似对字典进行.item()方法（得到key和对应的value）。
# 将年份存入list变量legislators中每行的第八列，按照append追加的方法
for i, row in enumerate(legislators):
row.append(birth_years[i])
# 将legislatros变量的第八列元素（出生年份）的字符串类型，强制类型转换成int类型。如遇到强制转换错误就将出生年份值变为0
for row in legislators:
try:
row[7] = int(row[7])
except Exception:
row[7] = 0
# 用字典进行姓名统计（key为姓名，value为出现次数）存入male_name_counts字典变量中。并将出现次数最多的名字（同样是最大出现次数，但名字不止一个），将这些名字存入list变量top_male_names中
top_male_names = []
male_name_counts = {}
# 用字典进行姓名统计，条件是出生年份大于1940，并且是女性
for row in legislators:
if row[7] > 1940 and row[3] == "M":
if row[1] in male_name_counts:
male_name_counts[row[1]] += 1
else:
male_name_counts[row[1]] = 1
# 找出名字出现最多的次数highest_value
highest_value = None
for key, value in male_name_counts.items():
if highest_value is None or value > highest_value:
highest_value = value
# 将名字次数出现最多的名字（同样是最大出现次数，但名字不止一个），将这些名字以追加append的方式存入list变量top_male_names中
for key, value in male_name_counts.items():
if value == highest_value:
top_male_names.append(key)
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案例6 Which airline is delayed the most?
这个案例来来回回做了好几天，反正基本上大都是参考答案做过的……酱油了……
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def column_number_from_name(column_name):
column_number = None
for i, column in enumerate(column_names):
if column == column_name:
column_number = i
return column_number
def find_average_delay(carrier_name=None):
total_delayed_flights = 0
total_delay_time = 0
delay_time_column = column_number_from_name("arr_delay")
delay_number_column = column_number_from_name("arr_del15")
carrier_column = column_number_from_name("carrier")
for row in flight_delays:
if carrier_name is None or row[carrier_column] == carrier_name:
total_delayed_flights += float(row[delay_number_column])
total_delay_time += float(row[delay_time_column])
return total_delay_time / total_delayed_flights
delays_by_carrier = {}
carrier_column = column_number_from_name("carrier")
carriers = [row[carrier_column] for row in flight_delays]
unique_carriers = list(set(carriers))
for carrier in unique_carriers:
delays_by_carrier[carrier] = find_average_delay(carrier)
print(delays_by_carrier)
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附录1 逐行读取txt文件
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# 方法一
f = open("foo.txt") # 返回一个文件对象
line = f.readline() # 调用文件的 readline()方法
while line:
print line, # 后面跟 ‘,‘ 将忽略换行符
# print(line, end = ‘‘) # 在 Python 3中使用
line = f.readline()
f.close()
# 方法二
for line in open("foo.txt"):
print line
# 方法三
f = open("c:\\1.txt","r")
lines = f.readlines() #读取全部内容
for line in lines
print line
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