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Python中基本的读文件和简单数据处理

2018-04-22  吃草的鱼仔

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:

  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)
  2. Discover weather pattern in LA(for循环和if判断进行频数统计)
  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)
  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)
  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)
  6. Which airline is delayed the most?
  7. 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]

 

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)

 

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)

 

 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)

 

 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)

 

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)

 

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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