一：隨時間變化的線性曲線

除了最常見的，還可以設定分組，比如

from pandas import Series
from pandas import DataFrame
from pandas import TimeGrouper
from matplotlib import pyplot
series = Series.from_csv('daily-minimum-temperatures.csv', header=0)
groups = series.groupby(TimeGrouper('A'))
years = DataFrame()
for name, group in groups:
	years[name.year] = group.values
years.plot(subplots=True, legend=False)
pyplot.show()
 

每一行都是365天

二：直方圖或者密度圖

series.plot(kind='kde')--密度圖

series.hist（）--直方圖

三：箱線圖

series.boxplot（）

下面給出一個年份不同月的箱線圖

from pandas import Series
from pandas import DataFrame
from pandas import TimeGrouper
from matplotlib import pyplot
from pandas import concat
series = Series.from_csv('daily-minimum-temperatures.csv', header=0)
one_year = series['1990']
groups = one_year.groupby(TimeGrouper('M'))
months = concat([DataFrame(x[1].values) for x in groups], axis=1)
months = DataFrame(months)
months.columns = range(1,13)
months.boxplot()
pyplot.show()
 

四：熱力圖

現在橫座標代表每年中的某一天，縱座標代表某一年，另外一個值大小就用顏色表示

from pandas import Series
from pandas import DataFrame
from pandas import TimeGrouper
from matplotlib import pyplot
series = Series.from_csv('daily-minimum-temperatures.csv', header=0)
groups = series.groupby(TimeGrouper('A'))
years = DataFrame()
for name, group in groups:
	years[name.year] = group.values
years = years.T
pyplot.matshow(years, interpolation=None, aspect='auto')
pyplot.show()
 

當然也可以畫出月份和日期的熱力圖

from pandas import Series
from pandas import DataFrame
from pandas import TimeGrouper
from matplotlib import pyplot
from pandas import concat
series = Series.from_csv('daily-minimum-temperatures.csv', header=0)
one_year = series['1990']
groups = one_year.groupby(TimeGrouper('M'))
months = concat([DataFrame(x[1].values) for x in groups], axis=1)
months = DataFrame(months)
months.columns = range(1,13)
pyplot.matshow(months, interpolation=None, aspect='auto')
pyplot.show()

五：滯後項和原項之間的點圖

Pandas has a built-in function for exactly this called the lag plot. It plots the observation at time t on the x-axis and the lag1 observation (t-1) on the y-axis.

• If the points cluster along a diagonal line from the bottom-left to the top-right of the plot, it suggests a positive correlation relationship.主對角線--正相關
• If the points cluster along a diagonal line from the top-left to the bottom-right, it suggests a negative correlation relationship.次對角線--負相關
• Either relationship is good as they can be modeled.

More points tighter in to the diagonal line suggests a stronger relationship and more spread from the line suggests a weaker relationship.越分散--越不相關

from pandas import Series
from matplotlib import pyplot
from pandas.tools.plotting import lag_plot
series = Series.from_csv('daily-minimum-temperatures.csv', header=0)
lag_plot(series)
pyplot.show()

這裡只畫出來t和t-1的圖，當然也可以畫t和t-1，t和t-2,t和t-3...t和t-7

from pandas import Series
from pandas import DataFrame
from pandas import concat
from matplotlib import pyplot
from pandas.plotting import scatter_matrix
series = Series.from_csv('daily-minimum-temperatures.csv', header=0)
values = DataFrame(series.values)
lags = 7
columns = [values]
for i in range(1,(lags + 1)):
	columns.append(values.shift(i))
dataframe = concat(columns, axis=1)
columns = ['t+1']
for i in range(1,(lags + 1)):
	columns.append('t-' + str(i))
dataframe.columns = columns
pyplot.figure(1)
for i in range(1,(lags + 1)):
	ax = pyplot.subplot(240 + i)
	ax.set_title('t+1 vs t-' + str(i))
	pyplot.scatter(x=dataframe['t+1'].values, y=dataframe['t-'+str(i)].values)
pyplot.show()

六：自相關圖（和五的區別是這一步量化相關關係）

用的是pearson相關係數

from pandas import Series
from matplotlib import pyplot
from pandas.tools.plotting import autocorrelation_plot
series = Series.from_csv('daily-minimum-temperatures.csv', header=0)
autocorrelation_plot(series)
pyplot.show()

https://machinelearningmastery.com/time-series-data-visualization-with-python/