Python 从.txt文件读取统计信息并输出它们
我应该从.txt文件中获取某些信息并将其输出。这是我需要的信息：Python 从.txt文件读取统计信息并输出它们,python,python-3.x,Python,Python 3.x,我应该从.txt文件中获取某些信息并将其输出。这是我需要的信息： 人口最多的州 人口最少的州 平均州人口 德克萨斯州人口 下面的示例如下所示： Alabama AL 4802982 Alaska AK 721523 Arizona AZ 6412700 Arkansas AR 2926229 California CA 37341989 这是我的代码，它并没有真正执行我需要它执行的任何操作： def main(): # Open the StateCensus2010.txt fi
- 人口最多的州
- 人口最少的州
- 平均州人口
- 德克萨斯州人口
Alabama
AL
4802982
Alaska
AK
721523
Arizona
AZ
6412700
Arkansas
AR
2926229
California
CA
37341989
def main():
# Open the StateCensus2010.txt file.
census_file = open('StateCensus2010.txt', 'r')
# Read the state name
state_name = census_file.readline()
while state_name != '':
state_abv = census_file.readline()
population = int(census_file.readline())
state_name = state_name.rstrip('\n')
state_abv = state_abv.rstrip('\n')
print('State Name: ', state_name)
print('State Abv.: ', state_abv)
print('Population: ', population)
print()
state_name = census_file.readline()
census_file.close()
main()
states = []
for line in data:
states.append(
dict(state=line.strip(),
abbrev=next(data).strip(),
pop=int(next(data)),
)
)
df = pd.DataFrame(states)
print(df)
print('\nmax population:\n', df.ix[df['pop'].idxmax()])
print('\nmin population:\n', df.ix[df['pop'].idxmin()])
print('\navg population:\n', df['pop'].mean())
print('\nAZ population:\n', df[df.abbrev == 'AZ'])
from io import StringIO
data = StringIO(u'\n'.join([x.strip() for x in """
Alabama
AL
4802982
Alaska
AK
721523
Arizona
AZ
6412700
Arkansas
AR
2926229
California
CA
37341989
""".split('\n')[1:-1]]))
abbrev pop state
0 AL 4802982 Alabama
1 AK 721523 Alaska
2 AZ 6412700 Arizona
3 AR 2926229 Arkansas
4 CA 37341989 California
max population:
abbrev CA
pop 37341989
state California
Name: 4, dtype: object
min population:
abbrev AK
pop 721523
state Alaska
Name: 1, dtype: object
avg population:
10441084.6
AZ population:
abbrev pop state
2 AZ 6412700 Arizona
Traceback (most recent call last):
File "main.py", line 13, in <module>
if population > max_population:
TypeError: unorderable types: str() > int()
由于数据顺序一致；州名，州Abv，人口。所以你只需要读一次这些行，然后显示所有三个信息。下面是示例代码
average = 0.0
total = 0.0
state_min = 999999999999
state_max = 0
statename_min = ''
statename_max = ''
texas_population = 0
with open('StateCensus2010.txt','r') as file:
# split new line, '\n' here means newline
data = file.read().split('\n')
# get the length of the data by using len() method
# there are 50 states in the text file
# each states have 3 information stored,
# state name, state abreviation, population
# that's why length of data which is 150/3 = 50 states
state_total = len(data)/3
# this count is used as an index for the list
count = 0
for i in range(int(state_total)):
statename = data[count]
state_abv = data[count+1]
population = int(data[count+2])
print('Statename : ',statename)
print('State Abv : ',state_abv)
print('Population: ',population)
print()
# sum all states population
total += population
if population > state_max:
state_max = population
statename_max = statename
if population < state_min:
state_min = population
statename_min = statename
if statename == 'Texas':
texas_population = population
# add 3 because we want to jump to next state
# for example the first three lines is Alabama info
# the next three lines is Alaska info and so on
count += 3
# divide the total population with number of states
average = total/state_total
print(str(average))
print('Lowest population state :', statename_min)
print('Highest population state :', statename_max)
print('Texas population :', texas_population)
average=0.0
总计=0.0
状态_min=9999999
状态_max=0
statename_min=“”
statename_max=“”
德克萨斯州人口=0
打开（'StateCensus2010.txt'，'r'）作为文件：
#拆分新行，此处的“\n”表示新行
data=file.read（）.split（'\n'）
#使用len（）方法获取数据的长度
#文本文件中有50个状态
#每个州存储了3个信息，
#州名、州删节、人口
#这就是为什么数据长度为150/3=50个状态
总状态=长度（数据）/3
#此计数用作列表的索引
计数=0
对于范围内的i（int（state_total））：
statename=数据[计数]
状态=数据[计数+1]
人口=int（数据[计数+2]）
打印（'Statename:'，Statename）
打印（'State Abv:'，State_Abv）
打印（'总体：'，总体）
打印（）
#所有州人口总和
总数+=人口
如果总体>状态_max：
状态_max=总体
statename\u max=statename
如果人口average = 0.0
total = 0.0
state_min = 999999999999
state_max = 0
statename_min = ''
statename_max = ''
texas_population = 0
with open('StateCensus2010.txt','r') as file:
# split new line, '\n' here means newline
data = file.read().split('\n')
# get the length of the data by using len() method
# there are 50 states in the text file
# each states have 3 information stored,
# state name, state abreviation, population
# that's why length of data which is 150/3 = 50 states
state_total = len(data)/3
# this count is used as an index for the list
count = 0
for i in range(int(state_total)):
statename = data[count]
state_abv = data[count+1]
population = int(data[count+2])
print('Statename : ',statename)
print('State Abv : ',state_abv)
print('Population: ',population)
print()
# sum all states population
total += population
if population > state_max:
state_max = population
statename_max = statename
if population < state_min:
state_min = population
statename_min = statename
if statename == 'Texas':
texas_population = population
# add 3 because we want to jump to next state
# for example the first three lines is Alabama info
# the next three lines is Alaska info and so on
count += 3
# divide the total population with number of states
average = total/state_total
print(str(average))
print('Lowest population state :', statename_min)
print('Highest population state :', statename_max)
print('Texas population :', texas_population)
average=0.0
总计=0.0
状态_min=9999999
状态_max=0
statename_min=“”
statename_max=“”
德克萨斯州人口=0
打开（'StateCensus2010.txt'，'r'）作为文件：
#拆分新行，此处的“\n”表示新行
data=file.read（）.split（'\n'）
#使用len（）方法获取数据的长度
#文本文件中有50个状态
#每个州存储了3个信息，
#州名、州删节、人口
#这就是为什么数据长度为150/3=50个状态
总状态=长度（数据）/3
#此计数用作列表的索引
计数=0
对于范围内的i（int（state_total））：
statename=数据[计数]
状态=数据[计数+1]
人口=int（数据[计数+2]）
打印（'Statename:'，Statename）
打印（'State Abv:'，State_Abv）
打印（'总体：'，总体）
打印（）
#所有州人口总和
总数+=人口
如果总体>状态_max：
状态_max=总体
statename\u max=statename
如果人口states = []
for line in data:
states.append(
dict(state=line.strip(),
abbrev=next(data).strip(),
pop=int(next(data)),
)
)
df = pd.DataFrame(states)
print(df)
print('\nmax population:\n', df.ix[df['pop'].idxmax()])
print('\nmin population:\n', df.ix[df['pop'].idxmin()])
print('\navg population:\n', df['pop'].mean())
print('\nAZ population:\n', df[df.abbrev == 'AZ'])
from io import StringIO
data = StringIO(u'\n'.join([x.strip() for x in """
Alabama
AL
4802982
Alaska
AK
721523
Arizona
AZ
6412700
Arkansas
AR
2926229
California
CA
37341989
""".split('\n')[1:-1]]))
abbrev pop state
0 AL 4802982 Alabama
1 AK 721523 Alaska
2 AZ 6412700 Arizona
3 AR 2926229 Arkansas
4 CA 37341989 California
max population:
abbrev CA
pop 37341989
state California
Name: 4, dtype: object
min population:
abbrev AK
pop 721523
state Alaska
Name: 1, dtype: object
avg population:
10441084.6
AZ population:
abbrev pop state
2 AZ 6412700 Arizona
states = []
for line in data:
states.append(
dict(state=line.strip(),
abbrev=next(data).strip(),
pop=int(next(data)),
)
)
df = pd.DataFrame(states)
print(df)
print('\nmax population:\n', df.ix[df['pop'].idxmax()])
print('\nmin population:\n', df.ix[df['pop'].idxmin()])
print('\navg population:\n', df['pop'].mean())
print('\nAZ population:\n', df[df.abbrev == 'AZ'])
from io import StringIO
data = StringIO(u'\n'.join([x.strip() for x in """
Alabama
AL
4802982
Alaska
AK
721523
Arizona
AZ
6412700
Arkansas
AR
2926229
California
CA
37341989
""".split('\n')[1:-1]]))
abbrev pop state
0 AL 4802982 Alabama
1 AK 721523 Alaska
2 AZ 6412700 Arizona
3 AR 2926229 Arkansas
4 CA 37341989 California
max population:
abbrev CA
pop 37341989
state California
Name: 4, dtype: object
min population:
abbrev AK
pop 721523
state Alaska
Name: 1, dtype: object
avg population:
10441084.6
AZ population:
abbrev pop state
2 AZ 6412700 Arizona
states = []
for line in data:
states.append(
dict(state=line.strip(),
abbrev=next(data).strip(),
pop=int(next(data)),
)
)
df = pd.DataFrame(states)
print(df)
print('\nmax population:\n', df.ix[df['pop'].idxmax()])
print('\nmin population:\n', df.ix[df['pop'].idxmin()])
print('\navg population:\n', df['pop'].mean())
print('\nAZ population:\n', df[df.abbrev == 'AZ'])
from io import StringIO
data = StringIO(u'\n'.join([x.strip() for x in """
Alabama
AL
4802982
Alaska
AK
721523
Arizona
AZ
6412700
Arkansas
AR
2926229
California
CA
37341989
""".split('\n')[1:-1]]))
abbrev pop state
0 AL 4802982 Alabama
1 AK 721523 Alaska
2 AZ 6412700 Arizona
3 AR 2926229 Arkansas
4 CA 37341989 California
max population:
abbrev CA
pop 37341989
state California
Name: 4, dtype: object
min population:
abbrev AK
pop 721523
state Alaska
Name: 1, dtype: object
avg population:
10441084.6
AZ population:
abbrev pop state
2 AZ 6412700 Arizona
这个问题很容易解决 代码：
states = []
for line in data:
states.append(
dict(state=line.strip(),
abbrev=next(data).strip(),
pop=int(next(data)),
)
)
df = pd.DataFrame(states)
print(df)
print('\nmax population:\n', df.ix[df['pop'].idxmax()])
print('\nmin population:\n', df.ix[df['pop'].idxmin()])
print('\navg population:\n', df['pop'].mean())
print('\nAZ population:\n', df[df.abbrev == 'AZ'])
from io import StringIO
data = StringIO(u'\n'.join([x.strip() for x in """
Alabama
AL
4802982
Alaska
AK
721523
Arizona
AZ
6412700
Arkansas
AR
2926229
California
CA
37341989
""".split('\n')[1:-1]]))
abbrev pop state
0 AL 4802982 Alabama
1 AK 721523 Alaska
2 AZ 6412700 Arizona
3 AR 2926229 Arkansas
4 CA 37341989 California
max population:
abbrev CA
pop 37341989
state California
Name: 4, dtype: object
min population:
abbrev AK
pop 721523
state Alaska
Name: 1, dtype: object
avg population:
10441084.6
AZ population:
abbrev pop state
2 AZ 6412700 Arizona
states = []
for line in data:
states.append(
dict(state=line.strip(),
abbrev=next(data).strip(),
pop=int(next(data)),
)
)
df = pd.DataFrame(states)
print(df)
print('\nmax population:\n', df.ix[df['pop'].idxmax()])
print('\nmin population:\n', df.ix[df['pop'].idxmin()])
print('\navg population:\n', df['pop'].mean())
print('\nAZ population:\n', df[df.abbrev == 'AZ'])
from io import StringIO
data = StringIO(u'\n'.join([x.strip() for x in """
Alabama
AL
4802982
Alaska
AK
721523
Arizona
AZ
6412700
Arkansas
AR
2926229
California
CA
37341989
""".split('\n')[1:-1]]))
abbrev pop state
0 AL 4802982 Alabama
1 AK 721523 Alaska
2 AZ 6412700 Arizona
3 AR 2926229 Arkansas
4 CA 37341989 California
max population:
abbrev CA
pop 37341989
state California
Name: 4, dtype: object
min population:
abbrev AK
pop 721523
state Alaska
Name: 1, dtype: object
avg population:
10441084.6
AZ population:
abbrev pop state
2 AZ 6412700 Arizona
states = []
for line in data:
states.append(
dict(state=line.strip(),
abbrev=next(data).strip(),
pop=int(next(data)),
)
)
df = pd.DataFrame(states)
print(df)
print('\nmax population:\n', df.ix[df['pop'].idxmax()])
print('\nmin population:\n', df.ix[df['pop'].idxmin()])
print('\navg population:\n', df['pop'].mean())
print('\nAZ population:\n', df[df.abbrev == 'AZ'])
from io import StringIO
data = StringIO(u'\n'.join([x.strip() for x in """
Alabama
AL
4802982
Alaska
AK
721523
Arizona
AZ
6412700
Arkansas
AR
2926229
California
CA
37341989
""".split('\n')[1:-1]]))
abbrev pop state
0 AL 4802982 Alabama
1 AK 721523 Alaska
2 AZ 6412700 Arizona
3 AR 2926229 Arkansas
4 CA 37341989 California
max population:
abbrev CA
pop 37341989
state California
Name: 4, dtype: object
min population:
abbrev AK
pop 721523
state Alaska
Name: 1, dtype: object
avg population:
10441084.6
AZ population:
abbrev pop state
2 AZ 6412700 Arizona
请尝试此操作，因为前面的代码与python 3不兼容。它支持python 2.7
def extract_data(state):
total_population = 0
for states, stats in state.items():
population = stats.get('population')
state_name = stats.get('state_name')
states = states
total_population = population + total_population
if 'highest' not in vars():
highest = population
higherst_state_name = state_name
highest_state = states
if 'lowest' not in vars():
lowest = population
lowest_state_name = state_name
lowest_state = states
if highest < population:
highest = population
higherst_state_name = state_name
highest_state = states
if lowest > population:
lowest = population
lowest_state_name = state_name
lowest_state = states
print(highest_state, highest)
print(lowest_state, lowest)
print(len(state))
print(int(total_population/len(state)))
print(state.get('TX').get('population'))
def main():
# Open the StateCensus2010.txt file.
census_file = open('states.txt', 'r')
# Read the state name
state_name = census_file.readline()
state = {}
while state_name != '':
state_abv = census_file.readline()
population = int(census_file.readline())
state_name = state_name.rstrip('\n')
state_abv = state_abv.rstrip('\n')
if state_abv in state:
state[state_abv].update({'population': population, 'state_name': state_name})
else:
state.setdefault(state_abv,{'population': population, 'state_name': state_name})
state_name = census_file.readline()
census_file.close()
return state
state=main()
extract_data(state)
def提取数据（状态）：
总人口=0
对于状态，stats在state.items（）中：
population=stats.get（'population'）
state\u name=stats.get（'state\u name'））
状态=状态
总人口=人口+总人口
如果变量（）中没有“最高”：
最高=人口
高级状态名称=状态名称
最高状态=状态
如果变量（）中没有“最低”：
最低=人口
最低州名=州名
最低状态=状态
如果最高值<人口：
最高=人口
高级状态名称=状态名称
最高状态=状态
如果最低>总体：
最低=人口
最低州名=州名
最低状态=状态
打印（最高_状态，最高）
打印（最低_状态，最低）
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