integer index: In [3]: s = pd.Series([1, 3, 5, np.nan, 6, 8]) In [4]: s Out[4]: 0 1.0 1 3.0 2 5.0 3 NaN 4 6.0 5 8.0 dtype: float64 Creating a DataFrame by passing a NumPy array, with a datetime 000521 5 3.0 2013-01-05 0.139488 -0.259328 1.082034 5 4.0 2013-01-06 -0.130327 -0.372906 1.072236 5 5.0 A where operation with setting. In [52]: df2 = df.copy() In [53]: df2[df2 > 0] = -df2 In [54]: -3.0 2013-01-05 -0.139488 -0.259328 -1.082034 -5 -4.0 2013-01-06 -0.130327 -0.372906 -1.072236 -5 -5.0 22 Chapter 3. Getting started pandas: powerful Python data analysis toolkit, Release 0.25.3 3.2

= df.groupby(["by1", "by2"]) In [11]: g[["v1", "v2"]].mean() Out[11]: v1 v2 by1 by2 1 95 5.0 55.0 99 5.0 55.0 2 95 7.0 77.0 99 NaN NaN big damp 3.0 33.0 blue dry 3.0 33.0 red red 4.0 44.0 wet np.ndenumerate(a)]) Out[29]: 0 1 2 3 0 0 0 0 1.0 1 0 0 1 2.0 2 0 0 2 3.0 3 0 0 3 4.0 4 0 1 0 5.0 .. .. .. .. ... 19 1 1 3 20.0 20 1 2 0 21.0 21 1 2 1 22.0 22 1 2 2 23.0 23 1 2 3 NaN [24 rows index="Animal", columns="FeedType", aggfunc= ˓→"sum") Out[39]: FeedType A B Animal Animal1 10.0 5.0 Animal2 2.0 13.0 Animal3 6.0 NaN The second approach is to use the groupby() method: In [40]: df

= df.groupby(["by1", "by2"]) In [11]: g[["v1", "v2"]].mean() Out[11]: v1 v2 by1 by2 1 95 5.0 55.0 99 5.0 55.0 2 95 7.0 77.0 99 NaN NaN big damp 3.0 33.0 blue dry 3.0 33.0 red red 4.0 44.0 wet np.ndenumerate(a)]) Out[29]: 0 1 2 3 0 0 0 0 1.0 1 0 0 1 2.0 2 0 0 2 3.0 3 0 0 3 4.0 4 0 1 0 5.0 .. .. .. .. ... 19 1 1 3 20.0 20 1 2 0 21.0 21 1 2 1 22.0 22 1 2 2 23.0 23 1 2 3 NaN [24 rows index="Animal", columns="FeedType", aggfunc="sum ˓→") Out[39]: FeedType A B Animal Animal1 10.0 5.0 Animal2 2.0 13.0 Animal3 6.0 NaN The second approach is to use the groupby() method: In [40]: df

= df.groupby(["by1", "by2"]) In [11]: g[["v1", "v2"]].mean() Out[11]: v1 v2 by1 by2 1 95 5.0 55.0 99 5.0 55.0 2 95 7.0 77.0 99 NaN NaN big damp 3.0 33.0 blue dry 3.0 33.0 red red 4.0 44.0 wet np.ndenumerate(a)]) Out[29]: 0 1 2 3 0 0 0 0 1.0 1 0 0 1 2.0 2 0 0 2 3.0 3 0 0 3 4.0 4 0 1 0 5.0 .. .. .. .. ... 19 1 1 3 20.0 20 1 2 0 21.0 21 1 2 1 22.0 22 1 2 2 23.0 23 1 2 3 NaN [24 rows index="Animal", columns="FeedType", aggfunc="sum ˓→") Out[39]: FeedType A B Animal Animal1 10.0 5.0 Animal2 2.0 13.0 Animal3 6.0 NaN The second approach is to use the groupby() method: In [40]: df

= df.groupby(['by1', 'by2']) In [11]: g[['v1', 'v2']].mean() Out[11]: v1 v2 by1 by2 1 95 5.0 55.0 99 5.0 55.0 2 95 7.0 77.0 99 NaN NaN big damp 3.0 33.0 blue dry 3.0 33.0 red red 4.0 44.0 wet np.ndenumerate(a)]) Out[29]: 0 1 2 3 0 0 0 0 1.0 1 0 0 1 2.0 2 0 0 2 3.0 3 0 0 3 4.0 4 0 1 0 5.0 .. .. .. .. ... 19 1 1 3 20.0 20 1 2 0 21.0 21 1 2 1 22.0 22 1 2 2 23.0 23 1 2 3 NaN [24 rows index='Animal', columns='FeedType', ....: aggfunc='sum') ....: Out[39]: FeedType A B Animal Animal1 10.0 5.0 Animal2 2.0 13.0 Animal3 6.0 NaN The second approach is to use the groupby() method: In [40]: df

= df.groupby(['by1', 'by2']) In [11]: g[['v1', 'v2']].mean() Out[11]: v1 v2 by1 by2 1 95 5.0 55.0 99 5.0 55.0 2 95 7.0 77.0 99 NaN NaN big damp 3.0 33.0 blue dry 3.0 33.0 red red 4.0 44.0 wet np.ndenumerate(a)]) Out[29]: 0 1 2 3 0 0 0 0 1.0 1 0 0 1 2.0 2 0 0 2 3.0 3 0 0 3 4.0 4 0 1 0 5.0 .. .. .. .. ... 19 1 1 3 20.0 20 1 2 0 21.0 21 1 2 1 22.0 22 1 2 2 23.0 23 1 2 3 NaN [24 rows index='Animal', columns='FeedType', ....: aggfunc='sum') ....: Out[39]: FeedType A B Animal Animal1 10.0 5.0 Animal2 2.0 13.0 Animal3 6.0 NaN The second approach is to use the groupby() method: In [40]: df

= df.groupby(["by1", "by2"]) In [11]: g[["v1", "v2"]].mean() Out[11]: v1 v2 by1 by2 1 95 5.0 55.0 99 5.0 55.0 2 95 7.0 77.0 99 NaN NaN big damp 3.0 33.0 blue dry 3.0 33.0 red red 4.0 44.0 wet np.ndenumerate(a)]) Out[29]: 0 1 2 3 0 0 0 0 1.0 1 0 0 1 2.0 2 0 0 2 3.0 3 0 0 3 4.0 4 0 1 0 5.0 .. .. .. .. ... 19 1 1 3 20.0 20 1 2 0 21.0 21 1 2 1 22.0 22 1 2 2 23.0 23 1 2 3 NaN [24 rows index="Animal", columns="FeedType", aggfunc="sum ˓→") Out[39]: FeedType A B Animal Animal1 10.0 5.0 Animal2 2.0 13.0 Animal3 6.0 NaN The second approach is to use the groupby() method: In [40]: df

= df.groupby(["by1", "by2"]) In [11]: g[["v1", "v2"]].mean() Out[11]: v1 v2 by1 by2 1 95 5.0 55.0 99 5.0 55.0 2 95 7.0 77.0 99 NaN NaN big damp 3.0 33.0 blue dry 3.0 33.0 red red 4.0 44.0 wet np.ndenumerate(a)]) Out[29]: 0 1 2 3 0 0 0 0 1.0 1 0 0 1 2.0 2 0 0 2 3.0 3 0 0 3 4.0 4 0 1 0 5.0 .. .. .. .. ... 19 1 1 3 20.0 20 1 2 0 21.0 21 1 2 1 22.0 22 1 2 2 23.0 23 1 2 3 NaN [24 rows index="Animal", columns="FeedType", aggfunc="sum ˓→") Out[39]: FeedType A B Animal Animal1 10.0 5.0 Animal2 2.0 13.0 Animal3 6.0 NaN The second approach is to use the groupby() method: In [40]: df

= df.groupby(["by1", "by2"]) In [11]: g[["v1", "v2"]].mean() Out[11]: v1 v2 by1 by2 1 95 5.0 55.0 99 5.0 55.0 2 95 7.0 77.0 99 NaN NaN big damp 3.0 33.0 blue dry 3.0 33.0 red red 4.0 44.0 wet np.ndenumerate(a)]) Out[29]: 0 1 2 3 0 0 0 0 1.0 1 0 0 1 2.0 2 0 0 2 3.0 3 0 0 3 4.0 4 0 1 0 5.0 .. .. .. .. ... 19 1 1 3 20.0 20 1 2 0 21.0 21 1 2 1 22.0 22 1 2 2 23.0 23 1 2 3 NaN [24 rows index="Animal", columns="FeedType", aggfunc= ˓→"sum") Out[39]: FeedType A B Animal Animal1 10.0 5.0 Animal2 2.0 13.0 Animal3 6.0 NaN The second approach is to use the groupby() method: In [40]: df

= df.groupby(["by1", "by2"]) In [11]: g[["v1", "v2"]].mean() Out[11]: v1 v2 by1 by2 1 95 5.0 55.0 99 5.0 55.0 2 95 7.0 77.0 99 NaN NaN big damp 3.0 33.0 blue dry 3.0 33.0 red red 4.0 44.0 wet np.ndenumerate(a)]) Out[29]: 0 1 2 3 0 0 0 0 1.0 1 0 0 1 2.0 2 0 0 2 3.0 3 0 0 3 4.0 4 0 1 0 5.0 .. .. .. .. ... 19 1 1 3 20.0 20 1 2 0 21.0 21 1 2 1 22.0 22 1 2 2 23.0 23 1 2 3 NaN [24 rows index="Animal", columns="FeedType", aggfunc= ˓→"sum") Out[39]: FeedType A B Animal Animal1 10.0 5.0 Animal2 2.0 13.0 Animal3 6.0 NaN The second approach is to use the groupby() method: In [40]: df