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# ndarray对象
import numpy as np
a = np.array([1,2,3])
print(type(a))
print("ndim:" + str(a.ndim) +"\nsize:" + str(a.size) + "\nshape:" + str(a.shape))
b = np.array([[1.3, 2.4], [0.3, 4.1]])
b.dtype
print("ndim:" + str(b.ndim) +"\nsize:" + str(b.size) + "\nshape:" + str(b.shape))
e = np.array([[1,2,3],(4,5,6),[7,8,9]])
e
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import numpy as np
f = np.array([[1,2,3], [4,5,6]], dtype=complex)
f
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import numpy as np
np.zeros((3,3))
np.arange(0,10)
#help(np.arange)
np.arange(0, 10, 2)
np.arange(0,12).reshape(3,4)
np.linspace(0,10,5)
np.random.random(3)
np.random.random((3,3))
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#
import numpy as np
import pandas as pd
data = pd.read_csv('monthly_csv_2.csv') # 读取csv文件
print(data)
print(type(data))
print("=============================================")
data = pd.read_csv('monthly_csv.csv', usecols=[0, 1, 2])
ret = data.sort_values(by='Value', ascending=False) # 根据value值降序
print(ret)
print("=============================================")
ret = data[(data['Value'] > 0.889)] # value值大于0.889
print(ret)
print("=============================================")
data_of_china = ret[ret['Country'] == 'China'] # 过滤Country为China的数据
print(data_of_china['Value'])
print("=============================================")
data_of_china = data[(data['Value'] > 0.889) & (data['Country'] == 'China')] # 过滤Country为China的数据
print(data_of_china)
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import numpy as np
import pandas as pd
import matplotlib as mpl
# mpl.use('TkAgg')
import matplotlib.pyplot as plt
%matplotlib inline
# 获取所有sum
data = pd.read_csv('res/monthly_csv.csv')
def get_value_from_data(data):
"""从dataframe中获取value列表"""
list_v1 = []
for value in data['Value']:
list_v1.append(value)
return list_v1
print("==================获取所有国家==================")
# 获取所有国家
ser_all_country = data.drop_duplicates(['Country']).reset_index(drop=True)['Country']
print("国家总数：" + str(len(ser_all_country)) + "个")
# print(ser_all_country['Country'])
print("==================获取Australia总和和平均值==================")
# 获取对应Australia国家的总和
print(data[(data['Country'] == ser_all_country[0])]['Value'].sum())
print(data[(data['Country'] == ser_all_country[0])]['Value'].mean())
print("==================获取所有国家的总和数据==================")
dict_sum = {}
for country in ser_all_country:
dict_sum[country] = (data[(data['Country'] == country)]['Value'])
variables = pd.DataFrame(dict_sum)
# variables.plot()
print("==================台湾,中国,澳大利亚 对比走势图==================")
# 长度必须是一致的
ret = data[(data['Country'] == 'Taiwan')][['Date','Value']]
list_tw = get_value_from_data(ret)[:400]
ret = data[(data['Country'] == 'China')][['Date','Value']]
list_ch = get_value_from_data(ret)[:400]
ret = data[(data['Country'] == 'Australia')][['Date','Value']]
list_aus = get_value_from_data(ret)[:400]
variables = pd.DataFrame({'China': np.array(list_ch),
'Taiwan': np.array(list_tw),
'Australia': np.array(list_aus)})
variables.plot()
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# print("==================画Australia的走势图==================")
# ret = data[(data['Country'] == 'Australia')]
# print(ret)
# rest_a = pd.Series(ret['Value'], index=ret['Date'])
# rest_a.plot(kind='bar', x=list_country)
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plt.plot([1,2,3,4])
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import datetime
import numpy as np
import matplotlib.pyplot as plt
import matplotlib.dates as mdates
months = mdates.MonthLocator()
days = mdates.DayLocator()
timeFmt = mdates.DateFormatter('%Y-%m')
events = [datetime.date(2015,1,23),
datetime.date(2015,1,28),
datetime.date(2015,2,3),
datetime.date(2015,2,21),
datetime.date(2015,3,15),
datetime.date(2015,3,24),
datetime.date(2015,4,8),
datetime.date(2015,4,24)]
readings = [12,22,25,20,18,15,17,14]
fig, ax = plt.subplots()
plt.plot(events, readings)
ax.xaxis.set_major_locator(months)
ax.xaxis.set_major_formatter(timeFmt)
ax.xaxis.set_minor_locator(days)
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import datetime as dt
import numpy as np
import pandas as pd
import matplotlib as mpl
# mpl.use('TkAgg')
import matplotlib.pyplot as plt
%matplotlib inline
def get_value_from_data(data, key):
"""从dataframe中获取value列表"""
list_v = []
for value in data[key]:
list_v.append(value)
return list_v
def get_age_from_birth(data):
import datetime as dt
now_year =dt.datetime.today().year #当前的年份
data['age']=now_year-data.Birth.dt.year
return data
# 读取csv文件
data = pd.read_csv('res/worldcup_team_csv.csv')
# data = pd.read_table('res/worldcup_team_csv.csv', sep=',')
data['Birth'] = pd.to_datetime(data['Birth'])
data = get_age_from_birth(data)
frame_data_russia = data[data['Country'] == 'Russia'];
frame_data_egypt = data[data['Country'] == 'Egypt'];
list_age = get_value_from_data(data[data['Country'] == 'Russia'][['age']], 'age')
list_age_egypt = get_value_from_data(frame_data_egypt, 'age')
variables = pd.DataFrame({'Russia': np.array(list_age),
'Egypt': np.array(list_age_egypt)})
variables.plot(kind='line')
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# 读取csv文件
# 没有表头，使用header选项，将其值置为None,pandas会为其添加默认表头
data = pd.read_csv('res/worldcup_team_csv.csv', header=None)
# 可以指定表头
data = pd.read_csv('res/worldcup_team_csv.csv', names=['white','red','blue','green','animal'])
print(data)
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# 创建等级结构的DataFrame对象
# 把所有想转换为索引的列名称赋给index_col
pd.read_csv('res/myCSV_03.csv', index_col=['color','status'])
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# pd.read_table('res/ch05_05.txt', sep='\D*', names=['white','red','blue'])
pd.read_table('res/ch05_05.txt', sep='\D*', names=['white','red','blue'], skiprows=[3])
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pd.read_table('res/ch05_06.txt', sep=',', skiprows=[0,1,3,6])
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# 从txt文件读取部分数据
# 过滤第2行，从起始行往后读3行
pd.read_csv('res/ch05_02.csv',sep=',', skiprows=[2], nrows=3, header=None)
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out = pd.Series() # 每隔3行进行累加，并且把和插入到series对象中
i = 0
pieces = pd.read_csv('res/ch05_01.csv', chunksize=3)
for piece in pieces:
print(piece['white'])
out.at[i] = piece['white'].sum()
i = i + 1
out
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my_frame = pd.DataFrame({
'ball':[0,4,8,12],
'pen':[1,5,9,13],
'pencil':[2,6,10,14],
'paper':[3,7,11,15]
})
# 写入文件的时候，取消index和header选项
my_frame.to_csv('res/ch05_07.csv', index=False, header=False)
# 同理对DataFrame对象
frame1 = pd.DataFrame(np.arange(16).reshape((4,4)),
index=['red', 'blue', 'yellow', 'white'],
columns=['ball', 'pen', 'pencil', 'paper'])
print(frame1)
frame2 = pd.DataFrame(np.arange(12).reshape((4,3)),
index=['blue', 'green', 'white', 'yellow'],
columns=['mug', 'pen', 'ball'])
print(frame2)
frame3 = frame1+frame2
frame3.to_csv('res/ch05_08.csv', index=False, header=False, na_rep='NaN')
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# 天然气数据分析
import numpy as np
import pandas as pd
# 获取原始数据
data = pd.read_csv('res/natural-gas-daily.csv')
frame = pd.DataFrame(data[data['Price']>10.0])
from datetime import datetime
arr_date = frame['Date'].values
print(arr_date)
# print(datetime.strptime(arr_date[0], '%Y-%m-%d'))
import numpy as np
import matplotlib.pyplot as plt
import matplotlib.dates as mdates
months = mdates.MonthLocator()
days = mdates.DayLocator()
timeFmt = mdates.DateFormatter('%Y-%m')
prices = np.array(frame['Price'])
# dates = [datetime.strptime(date, '%Y-%m') for date in arr_date]
# print(dates)
plt.plot(prices)
plt.show()
# fig, ax = plt.subplots()
# plt.plot(prices)
# ax.xaxis.set_major_locator(months)
# ax.xaxis.set_major_formatter(timeFmt)
# ax.xaxis.set_minor_locator(days)
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