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  • pdf文档 pandas: powerful Python data analysis toolkit - 0.25

    notice, this list of conditions and the following disclaimer in the documentation and/or other materials provided with the distribution. * Neither the name of the copyright holder nor the names of its /tests/data/tips.csv') ...: In [4]: tips = pd.read_csv(url) In [5]: tips.head() Out[5]: total_bill tip sex smoker day time size 0 16.99 1.01 Female No Sun Dinner 2 1 10.34 1.66 Male No Sun Dinner columns): SELECT total_bill, tip, smoker, time FROM tips LIMIT 5; With pandas, column selection is done by passing a list of column names to your DataFrame: In [6]: tips[['total_bill', 'tip', 'smoker', 'time']]
    0 码力 | 698 页 | 4.91 MB | 1 年前
    3
  • pdf文档 pandas: powerful Python data analysis toolkit - 0.17.0

    notice, this list of conditions and the following disclaimer in the documentation and/or other materials provided with the distribution. * Neither the name of the copyright holder nor the names of any Out[184]: In [185]: plot = rplot.RPlot(tips_data, x='total_bill', y='tip') In [186]: plot.add(rplot.TrellisGrid(['sex', 'smoker'])) In [187]: plot.add(rplot.GeomHistogram()) import seaborn as sns g = sns.FacetGrid(tips_data, row="sex", col="smoker") g.map(plt.hist, "total_bill") 742 Chapter 23. Plotting pandas: powerful Python data analysis toolkit, Release 0.17.0 Example
    0 码力 | 1787 页 | 10.76 MB | 1 年前
    3
  • pdf文档 pandas: powerful Python data analysis toolkit - 1.5.0rc0

    notice, this list of conditions and the following disclaimer in the documentation and/or other materials provided with the distribution. * Neither the name of the copyright holder nor the names of its /io/data/csv/tips.csv" ...: ) ...: In [4]: tips = pd.read_csv(url) In [5]: tips Out[5]: total_bill tip sex smoker day time size 0 16.99 1.01 Female No Sun Dinner 2 1 10.34 1.66 Male No Sun Dinner columns): SELECT total_bill, tip, smoker, time FROM tips; With pandas, column selection is done by passing a list of column names to your DataFrame: In [6]: tips[["total_bill", "tip", "smoker", "time"]]
    0 码力 | 3943 页 | 15.73 MB | 1 年前
    3
  • pdf文档 pandas: powerful Python data analysis toolkit - 0.12

    notice, this list of conditions and the following disclaimer in the documentation and/or other materials provided with the distribution. * Neither the name of the copyright holder nor the names of any plt.figure() In [3]: plot = rplot.RPlot(tips_data, x=’total_bill’, y=’tip’) In [4]: plot.add(rplot.TrellisGrid([’sex’, ’smoker’])) In [5]: plot.add(rplot.GeomHistogram()) plt.figure() In [8]: plot = rplot.RPlot(tips_data, x=’total_bill’, y=’tip’) In [9]: plot.add(rplot.TrellisGrid([’sex’, ’smoker’])) In [10]: plot.add(rplot.GeomDensity())
    0 码力 | 657 页 | 3.58 MB | 1 年前
    3
  • pdf文档 pandas: powerful Python data analysis toolkit - 0.20.3

    notice, this list of conditions and the following disclaimer in the documentation and/or other materials provided with the distribution. * Neither the name of the copyright holder nor the names of any das/tests/data/tips. ˓→csv' In [4]: tips = pd.read_csv(url) In [5]: tips.head() Out[5]: total_bill tip sex smoker day time size 0 16.99 1.01 Female No Sun Dinner 2 1 10.34 1.66 Male No Sun Dinner columns): SELECT total_bill, tip, smoker, time FROM tips LIMIT 5; With pandas, column selection is done by passing a list of column names to your DataFrame: In [6]: tips[['total_bill', 'tip', 'smoker', 'time']]
    0 码力 | 2045 页 | 9.18 MB | 1 年前
    3
  • pdf文档 pandas: powerful Python data analysis toolkit - 0.20.2

    notice, this list of conditions and the following disclaimer in the documentation and/or other materials provided with the distribution. * Neither the name of the copyright holder nor the names of any das/tests/data/tips. ˓→csv' In [4]: tips = pd.read_csv(url) In [5]: tips.head() Out[5]: total_bill tip sex smoker day time size 0 16.99 1.01 Female No Sun Dinner 2 1 10.34 1.66 Male No Sun Dinner columns): SELECT total_bill, tip, smoker, time FROM tips LIMIT 5; With pandas, column selection is done by passing a list of column names to your DataFrame: In [6]: tips[['total_bill', 'tip', 'smoker', 'time']]
    0 码力 | 1907 页 | 7.83 MB | 1 年前
    3
  • pdf文档 pandas: powerful Python data analysis toolkit - 0.19.0

    notice, this list of conditions and the following disclaimer in the documentation and/or other materials provided with the distribution. * Neither the name of the copyright holder nor the names of any /pandas/tests/data/tips.csv' In [4]: tips = pd.read_csv(url) In [5]: tips.head() Out[5]: total_bill tip sex smoker day time size 0 16.99 1.01 Female No Sun Dinner 2 1 10.34 1.66 Male No Sun Dinner columns): SELECT total_bill, tip, smoker, time FROM tips LIMIT 5; With pandas, column selection is done by passing a list of column names to your DataFrame: In [6]: tips[['total_bill', 'tip', 'smoker', 'time']]
    0 码力 | 1937 页 | 12.03 MB | 1 年前
    3
  • pdf文档 pandas: powerful Python data analysis toolkit - 0.19.1

    notice, this list of conditions and the following disclaimer in the documentation and/or other materials provided with the distribution. * Neither the name of the copyright holder nor the names of any das/tests/data/tips. ˓→csv' In [4]: tips = pd.read_csv(url) In [5]: tips.head() Out[5]: total_bill tip sex smoker day time size 0 16.99 1.01 Female No Sun Dinner 2 1 10.34 1.66 Male No Sun Dinner columns): SELECT total_bill, tip, smoker, time FROM tips LIMIT 5; With pandas, column selection is done by passing a list of column names to your DataFrame: In [6]: tips[['total_bill', 'tip', 'smoker', 'time']]
    0 码力 | 1943 页 | 12.06 MB | 1 年前
    3
  • pdf文档 pandas: powerful Python data analysis toolkit - 0.13.1

    notice, this list of conditions and the following disclaimer in the documentation and/or other materials provided with the distribution. * Neither the name of the copyright holder nor the names of any figure() Out[2]: In [3]: plot = rplot.RPlot(tips_data, x=’total_bill’, y=’tip’) In [4]: plot.add(rplot.TrellisGrid([’sex’, ’smoker’])) In [5]: plot.add(rplot.GeomHistogram()) figure() Out[7]: In [8]: plot = rplot.RPlot(tips_data, x=’total_bill’, y=’tip’) In [9]: plot.add(rplot.TrellisGrid([’sex’, ’smoker’])) In [10]: plot.add(rplot.GeomDensity())
    0 码力 | 1219 页 | 4.81 MB | 1 年前
    3
  • pdf文档 pandas: powerful Python data analysis toolkit - 0.21.1

    notice, this list of conditions and the following disclaimer in the documentation and/or other materials provided with the distribution. * Neither the name of the copyright holder nor the names of its das/tests/data/tips. ˓→csv' In [4]: tips = pd.read_csv(url) In [5]: tips.head() Out[5]: total_bill tip sex smoker day time size 0 16.99 1.01 Female No Sun Dinner 2 1 10.34 1.66 Male No Sun Dinner columns): SELECT total_bill, tip, smoker, time FROM tips LIMIT 5; With pandas, column selection is done by passing a list of column names to your DataFrame: In [6]: tips[['total_bill', 'tip', 'smoker', 'time']]
    0 码力 | 2207 页 | 8.59 MB | 1 年前
    3
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