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"matches_hh_level_sample = {key: np.random.choice(value) for key, value in matches_hh_level.items()}\n" + "matches_hh_level_sample = {key: np.random.choice(value) for key, value in matches_hh_level.items()}\n", + "\n", + "# remove items in list where value is nan\n", + "matches_hh_level_sample = {key: value for key, value in matches_hh_level_sample.items() if not pd.isna(value)}\n" + ] + }, + { + "cell_type": "code", + "execution_count": 37, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "('E02002183_0570', 2019001117.0)\n" + ] + } + ], + "source": [ + "print(list(matches_hh_level_sample.items())[568])" ] }, { "cell_type": "code", - "execution_count": 76, + "execution_count": 38, "metadata": {}, "outputs": [], "source": [ @@ -2398,7 +2569,7 @@ }, { "cell_type": "code", - "execution_count": 87, + "execution_count": 39, "metadata": {}, "outputs": [ { @@ -2432,7 +2603,176 @@ "5 rows × 190 columns
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365 rows × 15 columns
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