diff --git a/notebooks/ppc-examples-blank.ipynb b/notebooks/ppc-examples-blank.ipynb index 65d2196..0bb91fd 100644 --- a/notebooks/ppc-examples-blank.ipynb +++ b/notebooks/ppc-examples-blank.ipynb @@ -863,6 +863,25 @@ "campaign_weekday_performance[('Impressions', 0)]" ] }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "There is another useful function for pivoting - pivot_table. Pivot function needs to aggregate data before pivoting and doesn't allow to work with duplicate column values. With pivot_table function you can aggregate data in one step." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "campaign_weekday_performance = ad_group_performance.pivot_table(index=['CampaignName', 'DayOfWeek'],\n", + " values=['Impressions', 'Clicks', 'Cost', 'Conversions', 'ConversionsValue'],\n", + " aggfunc=np.sum)\n", + "campaign_weekday_performance" + ] + }, { "cell_type": "markdown", "metadata": {}, @@ -1088,7 +1107,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.6.1" + "version": "3.6.4" } }, "nbformat": 4, diff --git a/notebooks/ppc-examples.ipynb b/notebooks/ppc-examples.ipynb index 2048b35..97bc453 100644 --- a/notebooks/ppc-examples.ipynb +++ b/notebooks/ppc-examples.ipynb @@ -6039,7 +6039,7 @@ { "data": { "text/plain": [ - "" + "" ] }, "execution_count": 29, @@ -8648,6 +8648,289 @@ "campaign_weekday_performance[('Impressions', 0)]" ] }, + { + "cell_type": "code", + "execution_count": 45, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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ClicksConversionsConversionsValueCostImpressions
CampaignNameDayOfWeek
Sortiment0203397981164976.27101980.92213204
12826614652223564.59141042.37249400
25046834225173427.00250946.83432991
32550312251849827.56128431.33237585
417559553836560.9487293.71176450
581943352099.0840748.3699614
6570421474.8428267.8783824
Sport04529426724033629.71226536.94456675
16247041896317212.62313710.62535121
2109595871413030195.91546554.16926918
..................
43932621093172101.32197760.54379313
519120572870202.1896637.77215229
613664164248022.9568016.33179724
Značky06316744096621788.10316730.11623548
18574365819826545.92429348.45726377
21519531313519665149.08760073.461267391
37772757848737907.23387930.03690505
45492136305433887.02273310.26519119
52688613071943505.39132989.72290994
6196147111060276.4698028.66245236
\n", + "

21 rows × 5 columns

\n", + "
" + ], + "text/plain": [ + " Clicks Conversions ConversionsValue Cost \\\n", + "CampaignName DayOfWeek \n", + "Sortiment 0 20339 798 1164976.27 101980.92 \n", + " 1 28266 1465 2223564.59 141042.37 \n", + " 2 50468 3422 5173427.00 250946.83 \n", + " 3 25503 1225 1849827.56 128431.33 \n", + " 4 17559 553 836560.94 87293.71 \n", + " 5 8194 33 52099.08 40748.36 \n", + " 6 5704 2 1474.84 28267.87 \n", + "Sport 0 45294 2672 4033629.71 226536.94 \n", + " 1 62470 4189 6317212.62 313710.62 \n", + " 2 109595 8714 13030195.91 546554.16 \n", + "... ... ... ... ... \n", + " 4 39326 2109 3172101.32 197760.54 \n", + " 5 19120 572 870202.18 96637.77 \n", + " 6 13664 164 248022.95 68016.33 \n", + "Značky 0 63167 4409 6621788.10 316730.11 \n", + " 1 85743 6581 9826545.92 429348.45 \n", + " 2 151953 13135 19665149.08 760073.46 \n", + " 3 77727 5784 8737907.23 387930.03 \n", + " 4 54921 3630 5433887.02 273310.26 \n", + " 5 26886 1307 1943505.39 132989.72 \n", + " 6 19614 711 1060276.46 98028.66 \n", + "\n", + " Impressions \n", + "CampaignName DayOfWeek \n", + "Sortiment 0 213204 \n", + " 1 249400 \n", + " 2 432991 \n", + " 3 237585 \n", + " 4 176450 \n", + " 5 99614 \n", + " 6 83824 \n", + "Sport 0 456675 \n", + " 1 535121 \n", + " 2 926918 \n", + "... ... \n", + " 4 379313 \n", + " 5 215229 \n", + " 6 179724 \n", + "Značky 0 623548 \n", + " 1 726377 \n", + " 2 1267391 \n", + " 3 690505 \n", + " 4 519119 \n", + " 5 290994 \n", + " 6 245236 \n", + "\n", + "[21 rows x 5 columns]" + ] + }, + "execution_count": 45, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "ad_group_performance.pivot_table(index=['CampaignName', 'DayOfWeek'],\n", + " values=['Impressions', 'Clicks', 'Cost', 'Conversions', 'ConversionsValue'],\n", + " aggfunc=np.sum)" + ] + }, { "cell_type": "markdown", "metadata": {}, @@ -8661,7 +8944,7 @@ }, { "cell_type": "code", - "execution_count": 45, + "execution_count": 46, "metadata": {}, "outputs": [ { @@ -8960,7 +9243,7 @@ "[139101 rows x 7 columns]" ] }, - "execution_count": 45, + "execution_count": 46, "metadata": {}, "output_type": "execute_result" } @@ -8983,7 +9266,7 @@ }, { "cell_type": "code", - "execution_count": 46, + "execution_count": 47, "metadata": {}, "outputs": [ { @@ -9132,7 +9415,7 @@ "[699 rows x 1 columns]" ] }, - "execution_count": 46, + "execution_count": 47, "metadata": {}, "output_type": "execute_result" } @@ -9172,7 +9455,7 @@ }, { "cell_type": "code", - "execution_count": 47, + "execution_count": 48, "metadata": {}, "outputs": [ { @@ -9538,7 +9821,7 @@ "[699 rows x 9 columns]" ] }, - "execution_count": 47, + "execution_count": 48, "metadata": {}, "output_type": "execute_result" } @@ -9569,7 +9852,7 @@ }, { "cell_type": "code", - "execution_count": 48, + "execution_count": 49, "metadata": {}, "outputs": [ { @@ -9957,7 +10240,7 @@ "[699 rows x 10 columns]" ] }, - "execution_count": 48, + "execution_count": 49, "metadata": {}, "output_type": "execute_result" } @@ -9984,7 +10267,7 @@ }, { "cell_type": "code", - "execution_count": 49, + "execution_count": 50, "metadata": {}, "outputs": [], "source": [ @@ -10003,7 +10286,7 @@ }, { "cell_type": "code", - "execution_count": 50, + "execution_count": 51, "metadata": {}, "outputs": [], "source": [ @@ -10033,7 +10316,7 @@ }, { "cell_type": "code", - "execution_count": 51, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -10083,7 +10366,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.6.1" + "version": "3.6.4" } }, "nbformat": 4,