pandas: powerful Python data analysis toolkit - 0.14.0alpha=0.2) Out[6]:ec> In [7]: s.plot(kind=’kde’) Out[7]: ec> 106 Chapter 1. What’s New pandas: powerful Python data analysis 0x10c1244c>, , ec>, ], ’boxes’: [ array([[ , ec>], [ , ]] 0 码力 | 1349 页 | 7.67 MB | 1 年前3- pandas: powerful Python data analysis toolkit - 0.15.1rolling_sum(df, 60).plot(subplots=True) Out[44]: array([
ec>, , , ec>], ’means’: [], ’whiskers’: [ , ]}), (’B’, {’boxes’: [ ec>, ], ’fliers’: [ 0 码力 | 1557 页 | 9.10 MB | 1 年前3 - pandas: powerful Python data analysis toolkit - 0.17.0{'boxes': [
, ec>], 'fliers': [ , ] 'medians': [ , ec>], 'means': [], 'whiskers': [ , , ec>, , ]}) 0 码力 | 1787 页 | 10.76 MB | 1 年前3 - pandas: powerful Python data analysis toolkit - 0.15random.randn(1000) * 0.5 + 3))) ...: In [5]: plt.figure() Out[5]:
ec> In [6]: s.hist(normed=True, alpha=0.2) Out[6]: In [61]: correls.ix[:, ’A’, ’C’].plot() Out[61]: ec> 14.3 Expanding window moment functions A common alternative to rolling statistics is to use an expanding interpolate(method=m) for m in methods}) In [73]: plt.figure() Out[73]: ec> In [74]: df.plot() Out[74]: 426 Chapter 15 0 码力 | 1579 页 | 9.15 MB | 1 年前3 - pandas: powerful Python data analysis toolkit - 0.25---------------------------- ImportError Traceback (most recent call last)
----> 1 df.to_feather('example.feather') (continues on next page) 4.1. IO tools ec4f890> In [122]: df.B.plot(secondary_y=True, style='g') Out[122]: ec66150> 586 Chapter 4. User Guide pandas: 0 码力 | 698 页 | 4.91 MB | 1 年前3 - pandas: powerful Python data analysis toolkit - 0.13.1In [37]: df[’B’].plot(ax=axes[0,1]); axes[0,1].set_title(’B’) Out[37]:
ec550> In [38]: df[’C’].plot(ax=axes[1,0]); axes[1,0].set_title(’C’) Out[38]: ec350> In [101]: df.plot(colormap=cm.jet) Out[101]: 440 from_csv classmethod Series.from_csv(path, sep=’, ‘, parse_dates=True, header=None, index_col=0, en- coding=None, infer_datetime_format=False) Read delimited file into Series Parameters path : string file 0 码力 | 1219 页 | 4.81 MB | 1 年前3 - pandas: powerful Python data analysis toolkit - 0.19.0ts = ts.cumsum() In [137]: ts.plot() Out[137]:
ec10> On DataFrame, plot() is a convenience to plot all of the columns with labels: In [138]: df = pd loc='upper right', colWidths=[0.2, 0.2, 0.2]) .....: Out[161]: ec9a10> In [162]: df.plot(ax=ax, ylim=(0, 2), legend=None) Out[162]: coding=None, infer_datetime_format=False) Read CSV file (DISCOURAGED, please use pandas.read_csv() instead) 0 码力 | 1937 页 | 12.03 MB | 1 年前3 - pandas: powerful Python data analysis toolkit - 0.25.0weekday=MO(-1)>), Holiday: July 4th (month=7, day=4, observance=
ec21c80>), Holiday: Columbus Day (month=10, day=1, offset= ec21c80>), Holiday: Columbus Day (month=10, day=1, offset= 0 码力 | 2827 页 | 9.62 MB | 1 年前3 - pandas: powerful Python data analysis toolkit - 0.20.3\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\Out[122]:
ec18> 22.5. Plot Formatting 961 pandas: powerful Python data analysis toolkit, Release 0.20.3 22.5 from_csv classmethod Series.from_csv(path, sep=’, ‘, parse_dates=True, header=None, index_col=0, en- coding=None, infer_datetime_format=False) Read CSV file (DISCOURAGED, please use pandas.read_csv() instead) 0 码力 | 2045 页 | 9.18 MB | 1 年前3 - pandas: powerful Python data analysis toolkit - 0.20.2\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\Out[124]:
ec0b8> 22.5. Plot Formatting 957 pandas: powerful Python data analysis toolkit, Release 0.20.2 Using from_csv classmethod Series.from_csv(path, sep=’, ’, parse_dates=True, header=None, index_col=0, en- coding=None, infer_datetime_format=False) Read CSV file (DISCOURAGED, please use pandas.read_csv() instead) 0 码力 | 1907 页 | 7.83 MB | 1 年前3
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