diff --git a/tools_pandas.ipynb b/tools_pandas.ipynb index a12d2e1..27da1d5 100644 --- a/tools_pandas.ipynb +++ b/tools_pandas.ipynb @@ -283,7 +283,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "You can still access the items by integer location, like in a regular array:" + "To make it clear when you are accessing by label or by integer location, it is recommended to always use the `loc` attribute when accessing by label, and the `iloc` attribute when accessing by integer location:" ] }, { @@ -303,14 +303,7 @@ } ], "source": [ - "s2[1]" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "To make it clear when you are accessing by label or by integer location, it is recommended to always use the `loc` attribute when accessing by label, and the `iloc` attribute when accessing by integer location:" + "s2.loc[\"bob\"]" ] }, { @@ -329,26 +322,6 @@ "output_type": "execute_result" } ], - "source": [ - "s2.loc[\"bob\"]" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "83" - ] - }, - "execution_count": 11, - "metadata": {}, - "output_type": "execute_result" - } - ], "source": [ "s2.iloc[1]" ] @@ -362,7 +335,7 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 11, "metadata": {}, "outputs": [ { @@ -373,7 +346,7 @@ "dtype: int64" ] }, - "execution_count": 12, + "execution_count": 11, "metadata": {}, "output_type": "execute_result" } @@ -391,7 +364,7 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": 12, "metadata": {}, "outputs": [ { @@ -404,7 +377,7 @@ "dtype: int64" ] }, - "execution_count": 13, + "execution_count": 12, "metadata": {}, "output_type": "execute_result" } @@ -416,7 +389,7 @@ }, { "cell_type": "code", - "execution_count": 14, + "execution_count": 13, "metadata": {}, "outputs": [ { @@ -427,7 +400,7 @@ "dtype: int64" ] }, - "execution_count": 14, + "execution_count": 13, "metadata": {}, "output_type": "execute_result" } @@ -446,7 +419,7 @@ }, { "cell_type": "code", - "execution_count": 15, + "execution_count": 14, "metadata": {}, "outputs": [ { @@ -473,7 +446,7 @@ }, { "cell_type": "code", - "execution_count": 16, + "execution_count": 15, "metadata": {}, "outputs": [ { @@ -482,7 +455,7 @@ "1002" ] }, - "execution_count": 16, + "execution_count": 15, "metadata": {}, "output_type": "execute_result" } @@ -501,7 +474,7 @@ }, { "cell_type": "code", - "execution_count": 17, + "execution_count": 16, "metadata": {}, "outputs": [ { @@ -514,7 +487,7 @@ "dtype: int64" ] }, - "execution_count": 17, + "execution_count": 16, "metadata": {}, "output_type": "execute_result" } @@ -534,7 +507,7 @@ }, { "cell_type": "code", - "execution_count": 18, + "execution_count": 17, "metadata": {}, "outputs": [ { @@ -545,7 +518,7 @@ "dtype: int64" ] }, - "execution_count": 18, + "execution_count": 17, "metadata": {}, "output_type": "execute_result" } @@ -565,7 +538,7 @@ }, { "cell_type": "code", - "execution_count": 19, + "execution_count": 18, "metadata": {}, "outputs": [ { @@ -587,7 +560,7 @@ "dtype: float64" ] }, - "execution_count": 19, + "execution_count": 18, "metadata": {}, "output_type": "execute_result" } @@ -610,7 +583,7 @@ }, { "cell_type": "code", - "execution_count": 20, + "execution_count": 19, "metadata": {}, "outputs": [ { @@ -635,7 +608,7 @@ "dtype: float64" ] }, - "execution_count": 20, + "execution_count": 19, "metadata": {}, "output_type": "execute_result" } @@ -665,7 +638,7 @@ }, { "cell_type": "code", - "execution_count": 21, + "execution_count": 20, "metadata": {}, "outputs": [ { @@ -677,7 +650,7 @@ "dtype: int64" ] }, - "execution_count": 21, + "execution_count": 20, "metadata": {}, "output_type": "execute_result" } @@ -697,7 +670,7 @@ }, { "cell_type": "code", - "execution_count": 22, + "execution_count": 21, "metadata": {}, "outputs": [ { @@ -708,7 +681,7 @@ "Name: weights, dtype: int64" ] }, - "execution_count": 22, + "execution_count": 21, "metadata": {}, "output_type": "execute_result" } @@ -728,7 +701,7 @@ }, { "cell_type": "code", - "execution_count": 23, + "execution_count": 22, "metadata": { "scrolled": true }, @@ -776,7 +749,7 @@ }, { "cell_type": "code", - "execution_count": 24, + "execution_count": 23, "metadata": {}, "outputs": [ { @@ -791,13 +764,13 @@ " dtype='datetime64[ns]', freq='H')" ] }, - "execution_count": 24, + "execution_count": 23, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "dates = pd.date_range('2016/10/29 5:30pm', periods=12, freq='H')\n", + "dates = pd.date_range('2016/10/29 5:30pm', periods=12, freq='h')\n", "dates" ] }, @@ -810,7 +783,7 @@ }, { "cell_type": "code", - "execution_count": 25, + "execution_count": 24, "metadata": {}, "outputs": [ { @@ -831,7 +804,7 @@ "Freq: H, dtype: float64" ] }, - "execution_count": 25, + "execution_count": 24, "metadata": {}, "output_type": "execute_result" } @@ -850,7 +823,7 @@ }, { "cell_type": "code", - "execution_count": 26, + "execution_count": 25, "metadata": {}, "outputs": [ { @@ -881,23 +854,23 @@ }, { "cell_type": "code", - "execution_count": 27, + "execution_count": 26, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "DatetimeIndexResampler [freq=<2 * Hours>, axis=0, closed=left, label=left, convention=start, base=0]" + "" ] }, - "execution_count": 27, + "execution_count": 26, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "temp_series_freq_2H = temp_series.resample(\"2H\")\n", - "temp_series_freq_2H" + "temp_series_freq_2h = temp_series.resample(\"2h\")\n", + "temp_series_freq_2h" ] }, { @@ -909,11 +882,11 @@ }, { "cell_type": "code", - "execution_count": 28, + "execution_count": 27, "metadata": {}, "outputs": [], "source": [ - "temp_series_freq_2H = temp_series_freq_2H.mean()" + "temp_series_freq_2h = temp_series_freq_2h.mean()" ] }, { @@ -925,7 +898,7 @@ }, { "cell_type": "code", - "execution_count": 29, + "execution_count": 28, "metadata": {}, "outputs": [ { @@ -940,7 +913,7 @@ } ], "source": [ - "temp_series_freq_2H.plot(kind=\"bar\")\n", + "temp_series_freq_2h.plot(kind=\"bar\")\n", "plt.show()" ] }, @@ -951,6 +924,41 @@ "Note how the values have automatically been aggregated into 2-hour periods. If we look at the 6-8pm period, for example, we had a value of `5.1` at 6:30pm, and `6.1` at 7:30pm. After resampling, we just have one value of `5.6`, which is the mean of `5.1` and `6.1`. Rather than computing the mean, we could have used any other aggregation function, for example we can decide to keep the minimum value of each period:" ] }, + { + "cell_type": "code", + "execution_count": 29, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "2016-10-29 16:00:00 4.4\n", + "2016-10-29 18:00:00 5.1\n", + "2016-10-29 20:00:00 6.1\n", + "2016-10-29 22:00:00 5.7\n", + "2016-10-30 00:00:00 4.7\n", + "2016-10-30 02:00:00 3.9\n", + "2016-10-30 04:00:00 3.5\n", + "Freq: 2H, dtype: float64" + ] + }, + "execution_count": 29, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "temp_series_freq_2h = temp_series.resample(\"2h\").min()\n", + "temp_series_freq_2h" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Or, equivalently, we could use the `apply()` method instead:" + ] + }, { "cell_type": "code", "execution_count": 30, @@ -975,43 +983,8 @@ } ], "source": [ - "temp_series_freq_2H = temp_series.resample(\"2H\").min()\n", - "temp_series_freq_2H" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Or, equivalently, we could use the `apply()` method instead:" - ] - }, - { - "cell_type": "code", - "execution_count": 31, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "2016-10-29 16:00:00 4.4\n", - "2016-10-29 18:00:00 5.1\n", - "2016-10-29 20:00:00 6.1\n", - "2016-10-29 22:00:00 5.7\n", - "2016-10-30 00:00:00 4.7\n", - "2016-10-30 02:00:00 3.9\n", - "2016-10-30 04:00:00 3.5\n", - "Freq: 2H, dtype: float64" - ] - }, - "execution_count": 31, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "temp_series_freq_2H = temp_series.resample(\"2H\").apply(np.min)\n", - "temp_series_freq_2H" + "temp_series_freq_2h = temp_series.resample(\"2h\").apply(\"min\")\n", + "temp_series_freq_2h" ] }, { @@ -1024,7 +997,7 @@ }, { "cell_type": "code", - "execution_count": 32, + "execution_count": 31, "metadata": {}, "outputs": [ { @@ -1043,7 +1016,7 @@ "Freq: 15T, dtype: float64" ] }, - "execution_count": 32, + "execution_count": 31, "metadata": {}, "output_type": "execute_result" } @@ -1062,7 +1035,7 @@ }, { "cell_type": "code", - "execution_count": 33, + "execution_count": 32, "metadata": { "scrolled": true }, @@ -1083,7 +1056,7 @@ "Freq: 15T, dtype: float64" ] }, - "execution_count": 33, + "execution_count": 32, "metadata": {}, "output_type": "execute_result" } @@ -1095,7 +1068,7 @@ }, { "cell_type": "code", - "execution_count": 34, + "execution_count": 33, "metadata": {}, "outputs": [ { @@ -1126,7 +1099,7 @@ }, { "cell_type": "code", - "execution_count": 35, + "execution_count": 34, "metadata": {}, "outputs": [ { @@ -1144,10 +1117,10 @@ "2016-10-30 02:30:00-04:00 4.1\n", "2016-10-30 03:30:00-04:00 3.9\n", "2016-10-30 04:30:00-04:00 3.5\n", - "Freq: H, dtype: float64" + "dtype: float64" ] }, - "execution_count": 35, + "execution_count": 34, "metadata": {}, "output_type": "execute_result" } @@ -1168,7 +1141,7 @@ }, { "cell_type": "code", - "execution_count": 36, + "execution_count": 35, "metadata": {}, "outputs": [ { @@ -1186,10 +1159,10 @@ "2016-10-30 07:30:00+01:00 4.1\n", "2016-10-30 08:30:00+01:00 3.9\n", "2016-10-30 09:30:00+01:00 3.5\n", - "Freq: H, dtype: float64" + "dtype: float64" ] }, - "execution_count": 36, + "execution_count": 35, "metadata": {}, "output_type": "execute_result" } @@ -1208,7 +1181,7 @@ }, { "cell_type": "code", - "execution_count": 37, + "execution_count": 36, "metadata": {}, "outputs": [ { @@ -1226,10 +1199,10 @@ "2016-10-30 07:30:00 4.1\n", "2016-10-30 08:30:00 3.9\n", "2016-10-30 09:30:00 3.5\n", - "Freq: H, dtype: float64" + "dtype: float64" ] }, - "execution_count": 37, + "execution_count": 36, "metadata": {}, "output_type": "execute_result" } @@ -1248,7 +1221,7 @@ }, { "cell_type": "code", - "execution_count": 38, + "execution_count": 37, "metadata": {}, "outputs": [ { @@ -1256,7 +1229,7 @@ "output_type": "stream", "text": [ "\n", - "Cannot infer dst time from Timestamp('2016-10-30 02:30:00'), try using the 'ambiguous' argument\n" + "Cannot infer dst time from 2016-10-30 02:30:00, try using the 'ambiguous' argument\n" ] } ], @@ -1277,7 +1250,7 @@ }, { "cell_type": "code", - "execution_count": 39, + "execution_count": 38, "metadata": {}, "outputs": [ { @@ -1295,10 +1268,10 @@ "2016-10-30 07:30:00+01:00 4.1\n", "2016-10-30 08:30:00+01:00 3.9\n", "2016-10-30 09:30:00+01:00 3.5\n", - "Freq: H, dtype: float64" + "dtype: float64" ] }, - "execution_count": 39, + "execution_count": 38, "metadata": {}, "output_type": "execute_result" } @@ -1317,7 +1290,7 @@ }, { "cell_type": "code", - "execution_count": 40, + "execution_count": 39, "metadata": {}, "outputs": [ { @@ -1325,10 +1298,10 @@ "text/plain": [ "PeriodIndex(['2016Q1', '2016Q2', '2016Q3', '2016Q4', '2017Q1', '2017Q2',\n", " '2017Q3', '2017Q4'],\n", - " dtype='period[Q-DEC]', freq='Q-DEC')" + " dtype='period[Q-DEC]')" ] }, - "execution_count": 40, + "execution_count": 39, "metadata": {}, "output_type": "execute_result" } @@ -1347,7 +1320,7 @@ }, { "cell_type": "code", - "execution_count": 41, + "execution_count": 40, "metadata": {}, "outputs": [ { @@ -1355,10 +1328,10 @@ "text/plain": [ "PeriodIndex(['2016Q4', '2017Q1', '2017Q2', '2017Q3', '2017Q4', '2018Q1',\n", " '2018Q2', '2018Q3'],\n", - " dtype='period[Q-DEC]', freq='Q-DEC')" + " dtype='period[Q-DEC]')" ] }, - "execution_count": 41, + "execution_count": 40, "metadata": {}, "output_type": "execute_result" } @@ -1376,7 +1349,7 @@ }, { "cell_type": "code", - "execution_count": 42, + "execution_count": 41, "metadata": {}, "outputs": [ { @@ -1384,10 +1357,10 @@ "text/plain": [ "PeriodIndex(['2016-03', '2016-06', '2016-09', '2016-12', '2017-03', '2017-06',\n", " '2017-09', '2017-12'],\n", - " dtype='period[M]', freq='M')" + " dtype='period[M]')" ] }, - "execution_count": 42, + "execution_count": 41, "metadata": {}, "output_type": "execute_result" } @@ -1405,7 +1378,7 @@ }, { "cell_type": "code", - "execution_count": 43, + "execution_count": 42, "metadata": {}, "outputs": [ { @@ -1413,10 +1386,10 @@ "text/plain": [ "PeriodIndex(['2016-01', '2016-04', '2016-07', '2016-10', '2017-01', '2017-04',\n", " '2017-07', '2017-10'],\n", - " dtype='period[M]', freq='M')" + " dtype='period[M]')" ] }, - "execution_count": 43, + "execution_count": 42, "metadata": {}, "output_type": "execute_result" } @@ -1434,22 +1407,22 @@ }, { "cell_type": "code", - "execution_count": 44, + "execution_count": 43, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "PeriodIndex(['2016', '2016', '2016', '2016', '2017', '2017', '2017', '2017'], dtype='period[A-DEC]', freq='A-DEC')" + "PeriodIndex(['2016', '2016', '2016', '2016', '2017', '2017', '2017', '2017'], dtype='period[A-DEC]')" ] }, - "execution_count": 44, + "execution_count": 43, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "quarters.asfreq(\"A\")" + "quarters.asfreq(\"Y\")" ] }, { @@ -1461,7 +1434,7 @@ }, { "cell_type": "code", - "execution_count": 45, + "execution_count": 44, "metadata": {}, "outputs": [ { @@ -1478,7 +1451,7 @@ "Freq: Q-DEC, dtype: int64" ] }, - "execution_count": 45, + "execution_count": 44, "metadata": {}, "output_type": "execute_result" } @@ -1490,7 +1463,7 @@ }, { "cell_type": "code", - "execution_count": 46, + "execution_count": 45, "metadata": {}, "outputs": [ { @@ -1518,30 +1491,30 @@ }, { "cell_type": "code", - "execution_count": 47, + "execution_count": 46, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "2016-03-31 23:00:00 300\n", - "2016-06-30 23:00:00 320\n", - "2016-09-30 23:00:00 290\n", - "2016-12-31 23:00:00 390\n", - "2017-03-31 23:00:00 320\n", - "2017-06-30 23:00:00 360\n", - "2017-09-30 23:00:00 310\n", - "2017-12-31 23:00:00 410\n", - "Freq: Q-DEC, dtype: int64" + "2016-03-31 23:59:59.999999999 300\n", + "2016-06-30 23:59:59.999999999 320\n", + "2016-09-30 23:59:59.999999999 290\n", + "2016-12-31 23:59:59.999999999 390\n", + "2017-03-31 23:59:59.999999999 320\n", + "2017-06-30 23:59:59.999999999 360\n", + "2017-09-30 23:59:59.999999999 310\n", + "2017-12-31 23:59:59.999999999 410\n", + "dtype: int64" ] }, - "execution_count": 47, + "execution_count": 46, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "last_hours = quarterly_revenue.to_timestamp(how=\"end\", freq=\"H\")\n", + "last_hours = quarterly_revenue.to_timestamp(how=\"end\", freq=\"h\")\n", "last_hours" ] }, @@ -1554,7 +1527,7 @@ }, { "cell_type": "code", - "execution_count": 48, + "execution_count": 47, "metadata": {}, "outputs": [ { @@ -1571,7 +1544,7 @@ "Freq: Q-DEC, dtype: int64" ] }, - "execution_count": 48, + "execution_count": 47, "metadata": {}, "output_type": "execute_result" } @@ -1589,7 +1562,7 @@ }, { "cell_type": "code", - "execution_count": 49, + "execution_count": 48, "metadata": {}, "outputs": [ { @@ -1599,10 +1572,10 @@ " '2016-04-29 09:00', '2016-05-31 09:00', '2016-06-30 09:00',\n", " '2016-07-29 09:00', '2016-08-31 09:00', '2016-09-30 09:00',\n", " '2016-10-31 09:00', '2016-11-30 09:00', '2016-12-30 09:00'],\n", - " dtype='period[H]', freq='H')" + " dtype='period[H]')" ] }, - "execution_count": 49, + "execution_count": 48, "metadata": {}, "output_type": "execute_result" } @@ -1611,7 +1584,7 @@ "months_2016 = pd.period_range(\"2016\", periods=12, freq=\"M\")\n", "one_day_after_last_days = months_2016.asfreq(\"D\") + 1\n", "last_bdays = one_day_after_last_days.to_timestamp() - pd.tseries.offsets.BDay()\n", - "last_bdays.to_period(\"H\") + 9" + "last_bdays.to_period(\"h\") + 9" ] }, { @@ -1627,70 +1600,70 @@ }, { "cell_type": "code", - "execution_count": 50, + "execution_count": 49, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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\n", " \n", @@ -4195,7 +4168,7 @@ "charles True " ] }, - "execution_count": 83, + "execution_count": 82, "metadata": {}, "output_type": "execute_result" } @@ -4224,7 +4197,7 @@ }, { "cell_type": "code", - "execution_count": 84, + "execution_count": 83, "metadata": {}, "outputs": [ { @@ -4236,7 +4209,7 @@ "dtype: bool" ] }, - "execution_count": 84, + "execution_count": 83, "metadata": {}, "output_type": "execute_result" } @@ -4254,25 +4227,25 @@ }, { "cell_type": "code", - "execution_count": 85, + "execution_count": 84, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
\n", - "\n", "
\n", " \n", @@ -4329,7 +4302,7 @@ "charles NaN 185 112 26 False 5.0 32.724617" ] }, - "execution_count": 85, + "execution_count": 84, "metadata": {}, "output_type": "execute_result" } @@ -4348,25 +4321,25 @@ }, { "cell_type": "code", - "execution_count": 86, + "execution_count": 85, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
\n", - "\n", "
\n", " \n", @@ -4432,7 +4405,7 @@ "charles True " ] }, - "execution_count": 86, + "execution_count": 85, "metadata": {}, "output_type": "execute_result" } @@ -4453,25 +4426,25 @@ }, { "cell_type": "code", - "execution_count": 87, + "execution_count": 86, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
\n", - "\n", "
\n", " \n", @@ -4508,7 +4481,7 @@ "bob Dancing 181 83 34 True 0.0 25.335002 False" ] }, - "execution_count": 87, + "execution_count": 86, "metadata": {}, "output_type": "execute_result" } @@ -4527,25 +4500,25 @@ }, { "cell_type": "code", - "execution_count": 88, + "execution_count": 87, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
\n", - "\n", "
\n", " \n", @@ -4611,7 +4584,7 @@ "alice False " ] }, - "execution_count": 88, + "execution_count": 87, "metadata": {}, "output_type": "execute_result" } @@ -4629,25 +4602,25 @@ }, { "cell_type": "code", - "execution_count": 89, + "execution_count": 88, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
\n", - "\n", "
\n", " \n", @@ -4713,7 +4686,7 @@ "charles 112 " ] }, - "execution_count": 89, + "execution_count": 88, "metadata": {}, "output_type": "execute_result" } @@ -4732,25 +4705,25 @@ }, { "cell_type": "code", - "execution_count": 90, + "execution_count": 89, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
\n", - "\n", "
\n", " \n", @@ -4816,7 +4789,7 @@ "bob 83 " ] }, - "execution_count": 90, + "execution_count": 89, "metadata": {}, "output_type": "execute_result" } @@ -4838,7 +4811,7 @@ }, { "cell_type": "code", - "execution_count": 91, + "execution_count": 90, "metadata": {}, "outputs": [ { @@ -4867,7 +4840,7 @@ }, { "cell_type": "code", - "execution_count": 92, + "execution_count": 91, "metadata": { "scrolled": true }, @@ -4905,25 +4878,25 @@ }, { "cell_type": "code", - "execution_count": 93, + "execution_count": 92, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
\n", - "\n", "
\n", " \n", @@ -4971,7 +4944,7 @@ "darwin 9 10 10" ] }, - "execution_count": 93, + "execution_count": 92, "metadata": {}, "output_type": "execute_result" } @@ -4991,25 +4964,25 @@ }, { "cell_type": "code", - "execution_count": 94, + "execution_count": 93, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
\n", - "\n", "
\n", " \n", @@ -5057,7 +5030,7 @@ "darwin 3.000000 3.162278 3.162278" ] }, - "execution_count": 94, + "execution_count": 93, "metadata": {}, "output_type": "execute_result" } @@ -5075,25 +5048,25 @@ }, { "cell_type": "code", - "execution_count": 95, + "execution_count": 94, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
\n", - "\n", "
\n", " \n", @@ -5141,7 +5114,7 @@ "darwin 10 11 11" ] }, - "execution_count": 95, + "execution_count": 94, "metadata": {}, "output_type": "execute_result" } @@ -5159,25 +5132,25 @@ }, { "cell_type": "code", - "execution_count": 96, + "execution_count": 95, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
\n", - "\n", "
\n", " \n", @@ -5225,7 +5198,7 @@ "darwin True True True" ] }, - "execution_count": 96, + "execution_count": 95, "metadata": {}, "output_type": "execute_result" } @@ -5243,7 +5216,7 @@ }, { "cell_type": "code", - "execution_count": 97, + "execution_count": 96, "metadata": {}, "outputs": [ { @@ -5255,7 +5228,7 @@ "dtype: float64" ] }, - "execution_count": 97, + "execution_count": 96, "metadata": {}, "output_type": "execute_result" } @@ -5273,7 +5246,7 @@ }, { "cell_type": "code", - "execution_count": 98, + "execution_count": 97, "metadata": {}, "outputs": [ { @@ -5285,7 +5258,7 @@ "dtype: bool" ] }, - "execution_count": 98, + "execution_count": 97, "metadata": {}, "output_type": "execute_result" } @@ -5303,7 +5276,7 @@ }, { "cell_type": "code", - "execution_count": 99, + "execution_count": 98, "metadata": {}, "outputs": [ { @@ -5316,7 +5289,7 @@ "dtype: bool" ] }, - "execution_count": 99, + "execution_count": 98, "metadata": {}, "output_type": "execute_result" } @@ -5334,7 +5307,7 @@ }, { "cell_type": "code", - "execution_count": 100, + "execution_count": 99, "metadata": {}, "outputs": [ { @@ -5347,7 +5320,7 @@ "dtype: bool" ] }, - "execution_count": 100, + "execution_count": 99, "metadata": {}, "output_type": "execute_result" } @@ -5365,25 +5338,25 @@ }, { "cell_type": "code", - "execution_count": 101, + "execution_count": 100, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
\n", - "\n", "
\n", " \n", @@ -5431,7 +5404,7 @@ "darwin 1.25 1.25 2.5" ] }, - "execution_count": 101, + "execution_count": 100, "metadata": {}, "output_type": "execute_result" } @@ -5449,25 +5422,25 @@ }, { "cell_type": "code", - "execution_count": 102, + "execution_count": 101, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
\n", - "\n", "
\n", " \n", @@ -5515,7 +5488,7 @@ "darwin 7.75 8.75 7.5" ] }, - "execution_count": 102, + "execution_count": 101, "metadata": {}, "output_type": "execute_result" } @@ -5533,7 +5506,7 @@ }, { "cell_type": "code", - "execution_count": 103, + "execution_count": 102, "metadata": { "scrolled": true }, @@ -5542,18 +5515,18 @@ "data": { "text/html": [ "
\n", - "\n", "
\n", " \n", @@ -5601,7 +5574,7 @@ "darwin 1.0 2.0 2.0" ] }, - "execution_count": 103, + "execution_count": 102, "metadata": {}, "output_type": "execute_result" } @@ -5620,25 +5593,25 @@ }, { "cell_type": "code", - "execution_count": 104, + "execution_count": 103, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
\n", - "\n", "
\n", " \n", @@ -5686,7 +5659,7 @@ "charles 3.0 3.0 0.0" ] }, - "execution_count": 104, + "execution_count": 103, "metadata": {}, "output_type": "execute_result" } @@ -5699,7 +5672,7 @@ }, { "cell_type": "code", - "execution_count": 105, + "execution_count": 104, "metadata": { "scrolled": true }, @@ -5708,18 +5681,18 @@ "data": { "text/html": [ "
\n", - "\n", "
\n", " \n", @@ -5780,7 +5753,7 @@ "darwin NaN 11.0 10.0 NaN" ] }, - "execution_count": 105, + "execution_count": 104, "metadata": {}, "output_type": "execute_result" } @@ -5803,7 +5776,7 @@ }, { "cell_type": "code", - "execution_count": 106, + "execution_count": 105, "metadata": { "scrolled": true }, @@ -5812,18 +5785,18 @@ "data": { "text/html": [ "
\n", - "\n", "
\n", " \n", @@ -5884,7 +5857,7 @@ "darwin 0.0 11.0 10.0 0.0" ] }, - "execution_count": 106, + "execution_count": 105, "metadata": {}, "output_type": "execute_result" } @@ -5902,25 +5875,25 @@ }, { "cell_type": "code", - "execution_count": 107, + "execution_count": 106, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
\n", - "\n", "
\n", " \n", @@ -5981,7 +5954,7 @@ "darwin NaN 11.0 10.0 9.0" ] }, - "execution_count": 107, + "execution_count": 106, "metadata": {}, "output_type": "execute_result" } @@ -6004,25 +5977,25 @@ }, { "cell_type": "code", - "execution_count": 108, + "execution_count": 107, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
\n", - "\n", "
\n", " \n", @@ -6070,7 +6043,7 @@ "charles 3.0 3.0 0.0" ] }, - "execution_count": 108, + "execution_count": 107, "metadata": {}, "output_type": "execute_result" } @@ -6088,25 +6061,25 @@ }, { "cell_type": "code", - "execution_count": 109, + "execution_count": 108, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
\n", - "\n", "
\n", " \n", @@ -6154,7 +6127,7 @@ "charles 3.0 3.0 0.0" ] }, - "execution_count": 109, + "execution_count": 108, "metadata": {}, "output_type": "execute_result" } @@ -6172,25 +6145,25 @@ }, { "cell_type": "code", - "execution_count": 110, + "execution_count": 109, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
\n", - "\n", "
\n", " \n", @@ -6251,7 +6224,7 @@ "alice 0.0 0.0 0.0 0.0" ] }, - "execution_count": 110, + "execution_count": 109, "metadata": {}, "output_type": "execute_result" } @@ -6273,25 +6246,25 @@ }, { "cell_type": "code", - "execution_count": 111, + "execution_count": 110, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
\n", - "\n", "
\n", " \n", @@ -6352,7 +6325,7 @@ "darwin NaN 11.0 10.0 9.0" ] }, - "execution_count": 111, + "execution_count": 110, "metadata": {}, "output_type": "execute_result" } @@ -6370,7 +6343,7 @@ }, { "cell_type": "code", - "execution_count": 112, + "execution_count": 111, "metadata": { "scrolled": true }, @@ -6379,18 +6352,18 @@ "data": { "text/html": [ "
\n", - "\n", "
\n", " \n", @@ -6451,7 +6424,7 @@ "darwin 9.0 10.0 11.0 NaN" ] }, - "execution_count": 112, + "execution_count": 111, "metadata": {}, "output_type": "execute_result" } @@ -6471,25 +6444,25 @@ }, { "cell_type": "code", - "execution_count": 113, + "execution_count": 112, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
\n", - "\n", "
\n", " \n", @@ -6542,7 +6515,7 @@ "darwin 9.0 10.0 11.0 NaN" ] }, - "execution_count": 113, + "execution_count": 112, "metadata": {}, "output_type": "execute_result" } @@ -6561,25 +6534,25 @@ }, { "cell_type": "code", - "execution_count": 114, + "execution_count": 113, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
\n", - "\n", "
\n", " \n", @@ -6627,7 +6600,7 @@ "darwin 9.0 10.0 11.0" ] }, - "execution_count": 114, + "execution_count": 113, "metadata": {}, "output_type": "execute_result" } @@ -6649,7 +6622,7 @@ }, { "cell_type": "code", - "execution_count": 115, + "execution_count": 114, "metadata": { "scrolled": true }, @@ -6658,18 +6631,18 @@ "data": { "text/html": [ "
\n", - "\n", "
\n", " \n", @@ -6736,7 +6709,7 @@ "darwin 9.0 10.0 11.0 NaN Biking" ] }, - "execution_count": 115, + "execution_count": 114, "metadata": {}, "output_type": "execute_result" } @@ -6755,16 +6728,16 @@ }, { "cell_type": "code", - "execution_count": 116, + "execution_count": 115, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "" + "" ] }, - "execution_count": 116, + "execution_count": 115, "metadata": {}, "output_type": "execute_result" } @@ -6783,25 +6756,25 @@ }, { "cell_type": "code", - "execution_count": 117, + "execution_count": 116, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
\n", - "\n", "
\n", " \n", @@ -6846,7 +6819,7 @@ "Dancing 10.0 9.0 10.0 NaN" ] }, - "execution_count": 117, + "execution_count": 116, "metadata": {}, "output_type": "execute_result" } @@ -6872,25 +6845,25 @@ }, { "cell_type": "code", - "execution_count": 118, + "execution_count": 117, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
\n", - "\n", "
\n", " \n", @@ -6938,7 +6911,7 @@ "charles 3.0 3.0 0.0" ] }, - "execution_count": 118, + "execution_count": 117, "metadata": {}, "output_type": "execute_result" } @@ -6949,25 +6922,25 @@ }, { "cell_type": "code", - "execution_count": 119, + "execution_count": 118, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
\n", - "\n", "
\n", " \n", @@ -7084,7 +7057,7 @@ "11 darwin nov 11.0 0.0" ] }, - "execution_count": 119, + "execution_count": 118, "metadata": {}, "output_type": "execute_result" } @@ -7105,25 +7078,25 @@ }, { "cell_type": "code", - "execution_count": 120, + "execution_count": 119, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
\n", - "\n", "
\n", " \n", @@ -7172,13 +7145,13 @@ "darwin 0.333333 10.000000" ] }, - "execution_count": 120, + "execution_count": 119, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "pd.pivot_table(more_grades, index=\"name\")" + "pd.pivot_table(more_grades[[\"name\", \"grade\", \"bonus\"]], index=\"name\")" ] }, { @@ -7190,25 +7163,25 @@ }, { "cell_type": "code", - "execution_count": 121, + "execution_count": 120, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
\n", - "\n", "
\n", " \n", @@ -7257,13 +7230,13 @@ "darwin 1.0 11.0" ] }, - "execution_count": 121, + "execution_count": 120, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "pd.pivot_table(more_grades, index=\"name\", values=[\"grade\", \"bonus\"], aggfunc=np.max)" + "pd.pivot_table(more_grades, index=\"name\", values=[\"grade\", \"bonus\"], aggfunc=\"max\")" ] }, { @@ -7275,25 +7248,25 @@ }, { "cell_type": "code", - "execution_count": 122, + "execution_count": 121, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
\n", - "\n", "
\n", " \n", @@ -7362,7 +7335,7 @@ "All 8.75 9.5 7.75 8.666667" ] }, - "execution_count": 122, + "execution_count": 121, "metadata": {}, "output_type": "execute_result" } @@ -7380,25 +7353,25 @@ }, { "cell_type": "code", - "execution_count": 123, + "execution_count": 122, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
\n", - "\n", "
\n", " \n", @@ -7508,7 +7481,7 @@ "All 1.125 8.75" ] }, - "execution_count": 123, + "execution_count": 122, "metadata": {}, "output_type": "execute_result" } @@ -7527,25 +7500,25 @@ }, { "cell_type": "code", - "execution_count": 124, + "execution_count": 123, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
\n", - "\n", "
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\n", - "\n", "
\n", " \n", @@ -10033,7 +8635,7 @@ "bob Dancing 83.1 1984 3.0" ] }, - "execution_count": 129, + "execution_count": 128, "metadata": {}, "output_type": "execute_result" } @@ -10057,7 +8659,7 @@ }, { "cell_type": "code", - "execution_count": 130, + "execution_count": 129, "metadata": {}, "outputs": [], "source": [ @@ -10075,7 +8677,7 @@ }, { "cell_type": "code", - "execution_count": 131, + "execution_count": 130, "metadata": {}, "outputs": [ { @@ -10124,35 +8726,28 @@ } ], "source": [ + "from pathlib import Path\n", + "\n", "for filename in (\"my_df.csv\", \"my_df.html\", \"my_df.json\"):\n", " print(\"#\", filename)\n", - " with open(filename, \"rt\") as f:\n", - " print(f.read())\n", - " print()\n" + " print(Path(filename).read_text())\n", + " print()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "Note that the index is saved as the first column (with no name) in a CSV file, as `
` tags in HTML and as keys in JSON.\n", + "Note that the index is saved as the first column in a CSV file, as `` tags in HTML and as keys in JSON. The index column has no name by default, but you can fix that by setting `my_df.index.name` to any name you want.\n", "\n", "Saving to other formats works very similarly, but some formats require extra libraries to be installed. For example, saving to Excel requires the openpyxl library:" ] }, { "cell_type": "code", - "execution_count": 132, + "execution_count": 131, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "No module named 'openpyxl'\n" - ] - } - ], + "outputs": [], "source": [ "try:\n", " my_df.to_excel(\"my_df.xlsx\", sheet_name='People')\n", @@ -10170,25 +8765,25 @@ }, { "cell_type": "code", - "execution_count": 133, + "execution_count": 132, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
\n", - "\n", "\n", " \n", @@ -10225,7 +8820,7 @@ "bob Dancing 83.1 1984 3.0" ] }, - "execution_count": 133, + "execution_count": 132, "metadata": {}, "output_type": "execute_result" } @@ -10244,7 +8839,7 @@ }, { "cell_type": "code", - "execution_count": 134, + "execution_count": 133, "metadata": {}, "outputs": [ { @@ -10331,7 +8926,7 @@ "Columbia South Carolina 133358 34.000710 -81.034814" ] }, - "execution_count": 134, + "execution_count": 133, "metadata": {}, "output_type": "execute_result" } @@ -10366,25 +8961,25 @@ }, { "cell_type": "code", - "execution_count": 135, + "execution_count": 134, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
\n", - "\n", "
\n", " \n", @@ -10445,7 +9040,7 @@ "4 UT Salt Lake City 40.755851 -111.896657" ] }, - "execution_count": 135, + "execution_count": 134, "metadata": {}, "output_type": "execute_result" } @@ -10464,25 +9059,25 @@ }, { "cell_type": "code", - "execution_count": 136, + "execution_count": 135, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
\n", - "\n", "
\n", " \n", @@ -10530,7 +9125,7 @@ "6 2242193 Houston Texas" ] }, - "execution_count": 136, + "execution_count": 135, "metadata": {}, "output_type": "execute_result" } @@ -10555,25 +9150,25 @@ }, { "cell_type": "code", - "execution_count": 137, + "execution_count": 136, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
\n", - "\n", "
\n", " \n", @@ -10626,7 +9221,7 @@ "2 FL Miami 25.791100 -80.320733 413201 Florida" ] }, - "execution_count": 137, + "execution_count": 136, "metadata": {}, "output_type": "execute_result" } @@ -10646,25 +9241,25 @@ }, { "cell_type": "code", - "execution_count": 138, + "execution_count": 137, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
\n", - "\n", "
\n", " \n", @@ -10747,7 +9342,7 @@ "5 NaN Houston NaN NaN 2242193.0 Texas" ] }, - "execution_count": 138, + "execution_count": 137, "metadata": {}, "output_type": "execute_result" } @@ -10766,25 +9361,25 @@ }, { "cell_type": "code", - "execution_count": 139, + "execution_count": 138, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
\n", - "\n", "
\n", " \n", @@ -10847,7 +9442,7 @@ "3 NaN Houston NaN NaN 2242193 Texas" ] }, - "execution_count": 139, + "execution_count": 138, "metadata": {}, "output_type": "execute_result" } @@ -10865,25 +9460,25 @@ }, { "cell_type": "code", - "execution_count": 140, + "execution_count": 139, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
\n", - "\n", "
\n", " \n", @@ -10945,7 +9540,7 @@ "2 Florida " ] }, - "execution_count": 140, + "execution_count": 139, "metadata": {}, "output_type": "execute_result" } @@ -10966,128 +9561,128 @@ }, { "cell_type": "code", - "execution_count": 141, + "execution_count": 140, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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statecitylatlngpopulationstate
0CASan Francisco37.781334-122.416728NaNCA
1NYNew York40.705649-74.008344NaNNY
2FLMiami25.791100-80.320733NaNFL
3OHCleveland41.473508-81.739791NaNOH
4UTSalt Lake City40.755851-111.896657NaNUT
3CaliforniaSan FranciscoNaNNaN808976.0California
4New-YorkNew YorkNaNNaN8363710.0New-York
5FloridaMiamiNaNNaN413201.0Florida
6TexasHoustonNaNNaN2242193.0Texas
\n", "
" ], "text/plain": [ - " city lat lng population state\n", - "0 San Francisco 37.781334 -122.416728 NaN CA\n", - "1 New York 40.705649 -74.008344 NaN NY\n", - "2 Miami 25.791100 -80.320733 NaN FL\n", - "3 Cleveland 41.473508 -81.739791 NaN OH\n", - "4 Salt Lake City 40.755851 -111.896657 NaN UT\n", - "3 San Francisco NaN NaN 808976.0 California\n", - "4 New York NaN NaN 8363710.0 New-York\n", - "5 Miami NaN NaN 413201.0 Florida\n", - "6 Houston NaN NaN 2242193.0 Texas" + " state city lat lng population\n", + "0 CA San Francisco 37.781334 -122.416728 NaN\n", + "1 NY New York 40.705649 -74.008344 NaN\n", + "2 FL Miami 25.791100 -80.320733 NaN\n", + "3 OH Cleveland 41.473508 -81.739791 NaN\n", + "4 UT Salt Lake City 40.755851 -111.896657 NaN\n", + "3 California San Francisco NaN NaN 808976.0\n", + "4 New-York New York NaN NaN 8363710.0\n", + "5 Florida Miami NaN NaN 413201.0\n", + "6 Texas Houston NaN NaN 2242193.0" ] }, - "execution_count": 141, + "execution_count": 140, "metadata": {}, "output_type": "execute_result" } @@ -11106,65 +9701,65 @@ }, { "cell_type": "code", - "execution_count": 142, + "execution_count": 141, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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statecitylatlngpopulationstate
3OHCleveland41.473508-81.739791NaNOH
3CaliforniaSan FranciscoNaNNaN808976.0California
\n", "
" ], "text/plain": [ - " city lat lng population state\n", - "3 Cleveland 41.473508 -81.739791 NaN OH\n", - "3 San Francisco NaN NaN 808976.0 California" + " state city lat lng population\n", + "3 OH Cleveland 41.473508 -81.739791 NaN\n", + "3 California San Francisco NaN NaN 808976.0" ] }, - "execution_count": 142, + "execution_count": 141, "metadata": {}, "output_type": "execute_result" } @@ -11182,128 +9777,128 @@ }, { "cell_type": "code", - "execution_count": 143, + "execution_count": 142, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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statecitylatlngpopulationstate
0CASan Francisco37.781334-122.416728NaNCA
1NYNew York40.705649-74.008344NaNNY
2FLMiami25.791100-80.320733NaNFL
3OHCleveland41.473508-81.739791NaNOH
4UTSalt Lake City40.755851-111.896657NaNUT
5CaliforniaSan FranciscoNaNNaN808976.0California
6New-YorkNew YorkNaNNaN8363710.0New-York
7FloridaMiamiNaNNaN413201.0Florida
8TexasHoustonNaNNaN2242193.0Texas
\n", "
" ], "text/plain": [ - " city lat lng population state\n", - "0 San Francisco 37.781334 -122.416728 NaN CA\n", - "1 New York 40.705649 -74.008344 NaN NY\n", - "2 Miami 25.791100 -80.320733 NaN FL\n", - "3 Cleveland 41.473508 -81.739791 NaN OH\n", - "4 Salt Lake City 40.755851 -111.896657 NaN UT\n", - "5 San Francisco NaN NaN 808976.0 California\n", - "6 New York NaN NaN 8363710.0 New-York\n", - "7 Miami NaN NaN 413201.0 Florida\n", - "8 Houston NaN NaN 2242193.0 Texas" + " state city lat lng population\n", + "0 CA San Francisco 37.781334 -122.416728 NaN\n", + "1 NY New York 40.705649 -74.008344 NaN\n", + "2 FL Miami 25.791100 -80.320733 NaN\n", + "3 OH Cleveland 41.473508 -81.739791 NaN\n", + "4 UT Salt Lake City 40.755851 -111.896657 NaN\n", + "5 California San Francisco NaN NaN 808976.0\n", + "6 New-York New York NaN NaN 8363710.0\n", + "7 Florida Miami NaN NaN 413201.0\n", + "8 Texas Houston NaN NaN 2242193.0" ] }, - "execution_count": 143, + "execution_count": 142, "metadata": {}, "output_type": "execute_result" } @@ -11321,25 +9916,25 @@ }, { "cell_type": "code", - "execution_count": 144, + "execution_count": 143, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
\n", - "\n", "\n", " \n", @@ -11412,7 +10007,7 @@ "6 Texas Houston" ] }, - "execution_count": 144, + "execution_count": 143, "metadata": {}, "output_type": "execute_result" } @@ -11430,7 +10025,7 @@ }, { "cell_type": "code", - "execution_count": 145, + "execution_count": 144, "metadata": { "scrolled": true }, @@ -11439,18 +10034,18 @@ "data": { "text/html": [ "
\n", - "\n", "
\n", " \n", @@ -11560,7 +10155,7 @@ "6 Texas " ] }, - "execution_count": 145, + "execution_count": 144, "metadata": {}, "output_type": "execute_result" } @@ -11578,7 +10173,7 @@ }, { "cell_type": "code", - "execution_count": 146, + "execution_count": 145, "metadata": { "scrolled": true }, @@ -11587,18 +10182,18 @@ "data": { "text/html": [ "
\n", - "\n", "
\n", " \n", @@ -11610,9 +10205,41 @@ " \n", " \n", " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -11621,6 +10248,14 @@ " \n", " \n", " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -11628,53 +10263,22 @@ " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", " \n", "
populationstate
city
San FranciscoCA37.781334-122.416728808976.0California
New YorkNY40.705649-74.0083448363710.0New-York
MiamiFL25.791100-80.320733413201.0Florida
ClevelandOH41.473508NaN
Salt Lake CityUT40.755851-111.896657NaNNaN
HoustonNaNNaN2242193.0Texas
MiamiFL25.791100-80.320733413201.0Florida
New YorkNY40.705649-74.0083448363710.0New-York
Salt Lake CityUT40.755851-111.896657NaNNaN
San FranciscoCA37.781334-122.416728808976.0California
\n", "
" ], "text/plain": [ " state lat lng population state\n", - "Cleveland OH 41.473508 -81.739791 NaN NaN\n", - "Houston NaN NaN NaN 2242193.0 Texas\n", - "Miami FL 25.791100 -80.320733 413201.0 Florida\n", + "city \n", + "San Francisco CA 37.781334 -122.416728 808976.0 California\n", "New York NY 40.705649 -74.008344 8363710.0 New-York\n", + "Miami FL 25.791100 -80.320733 413201.0 Florida\n", + "Cleveland OH 41.473508 -81.739791 NaN NaN\n", "Salt Lake City UT 40.755851 -111.896657 NaN NaN\n", - "San Francisco CA 37.781334 -122.416728 808976.0 California" + "Houston NaN NaN NaN 2242193.0 Texas" ] }, - "execution_count": 146, + "execution_count": 145, "metadata": {}, "output_type": "execute_result" } @@ -11700,25 +10304,25 @@ }, { "cell_type": "code", - "execution_count": 148, + "execution_count": 146, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
\n", - "\n", "\n", " \n", @@ -11771,7 +10375,7 @@ "6 2242193 Houston Texas 20" ] }, - "execution_count": 148, + "execution_count": 146, "metadata": {}, "output_type": "execute_result" } @@ -11786,58 +10390,154 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "Right now the `eco_code` column is full of apparently meaningless codes. Let's fix that. First, we will create a new categorical column based on the `eco_code`s:" + "Right now the `eco_code` column is full of apparently meaningless codes. Let's fix that by creating a new categorical column based on the `eco_code`s:" + ] + }, + { + "cell_type": "code", + "execution_count": 147, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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populationcitystateeco_codeeconomy
3808976San FranciscoCalifornia17Banking
48363710New YorkNew-York17Banking
5413201MiamiFlorida34Tourism
62242193HoustonTexas20Energy
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" + ], + "text/plain": [ + " population city state eco_code economy\n", + "3 808976 San Francisco California 17 Banking\n", + "4 8363710 New York New-York 17 Banking\n", + "5 413201 Miami Florida 34 Tourism\n", + "6 2242193 Houston Texas 20 Energy" + ] + }, + "execution_count": 147, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "city_eco['economy'] = pd.Categorical(\n", + " city_eco['eco_code'].map({17: 'Banking', 20: 'Energy', 34: 'Tourism'})\n", + ")\n", + "city_eco" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "You can get a categorical column's list of categories like this:" + ] + }, + { + "cell_type": "code", + "execution_count": 148, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Index(['Banking', 'Energy', 'Tourism'], dtype='object')" + ] + }, + "execution_count": 148, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "city_eco['economy'].cat.categories" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "And you can rename categories using `rename_categories`:" ] }, { "cell_type": "code", "execution_count": 149, "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "Int64Index([17, 20, 34], dtype='int64')" - ] - }, - "execution_count": 149, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "city_eco[\"economy\"] = city_eco[\"eco_code\"].astype('category')\n", - "city_eco[\"economy\"].cat.categories" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Now we can give each category a meaningful name:" - ] - }, - { - "cell_type": "code", - "execution_count": 150, - "metadata": {}, "outputs": [ { "data": { "text/html": [ "
\n", - "\n", "\n", " \n", @@ -11895,110 +10595,16 @@ "6 2242193 Houston Texas 20 Energy" ] }, - "execution_count": 150, + "execution_count": 149, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "city_eco[\"economy\"].cat.categories = [\"Finance\", \"Energy\", \"Tourism\"]\n", + "city_eco['economy'] = city_eco['economy'].cat.rename_categories({'Banking': 'Finance'})\n", "city_eco" ] }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Note that categorical values are sorted according to their categorical order, *not* their alphabetical order:" - ] - }, - { - "cell_type": "code", - "execution_count": 151, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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populationcitystateeco_codeeconomy
5413201MiamiFlorida34Tourism
62242193HoustonTexas20Energy
48363710New YorkNew-York17Finance
3808976San FranciscoCalifornia17Finance
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" - ], - "text/plain": [ - " population city state eco_code economy\n", - "5 413201 Miami Florida 34 Tourism\n", - "6 2242193 Houston Texas 20 Energy\n", - "4 8363710 New York New-York 17 Finance\n", - "3 808976 San Francisco California 17 Finance" - ] - }, - "execution_count": 151, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "city_eco.sort_values(by=\"economy\", ascending=False)" - ] - }, { "cell_type": "markdown", "metadata": {}, @@ -12031,7 +10637,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.8.12" + "version": "3.9.10" }, "toc": { "toc_cell": false,