From 53b80c0ac14f3315ec7369df4310e39a7b6cffab Mon Sep 17 00:00:00 2001 From: Giovanni Colavizza Date: Fri, 15 Jul 2022 10:58:53 +0200 Subject: [PATCH] added some exercises to 3.2 --- ...data analysis and working with texts.ipynb | 537 +++++++++++++----- 1 file changed, 406 insertions(+), 131 deletions(-) diff --git a/notebooks/3.2 Exploratory data analysis and working with texts.ipynb b/notebooks/3.2 Exploratory data analysis and working with texts.ipynb index 650af24..34d44c9 100644 --- a/notebooks/3.2 Exploratory data analysis and working with texts.ipynb +++ b/notebooks/3.2 Exploratory data analysis and working with texts.ipynb @@ -51,7 +51,7 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 6, "metadata": {}, "outputs": [], "source": [ @@ -69,7 +69,7 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 7, "metadata": {}, "outputs": [ { @@ -368,8 +368,32 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 27, "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "1.0 9424\n", + "0.0 130\n", + "Name: m_gender, dtype: int64" + ] + }, + "execution_count": 27, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df_contracts.m_gender.value_counts()" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": { + "scrolled": true + }, "outputs": [ { "data": { @@ -462,7 +486,7 @@ "2 spezieri " ] }, - "execution_count": 6, + "execution_count": 8, "metadata": {}, "output_type": "execute_result" } @@ -491,7 +515,7 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 23, "metadata": {}, "outputs": [ { @@ -500,13 +524,13 @@ "" ] }, - "execution_count": 7, + "execution_count": 23, "metadata": {}, "output_type": "execute_result" }, { "data": { - "image/png": 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\n", 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\n", "text/plain": [ "
" ] @@ -518,12 +542,40 @@ } ], "source": [ - "df_contracts.annual_salary.hist(bins=100)" + "df_contracts.a_age.hist(bins=30)" ] }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 17, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "count 9303.000000\n", + "mean 14.266688\n", + "std 2.902770\n", + "min 1.000000\n", + "25% 12.000000\n", + "50% 14.000000\n", + "75% 16.000000\n", + "max 50.000000\n", + "Name: a_age, dtype: float64" + ] + }, + "execution_count": 17, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df_contracts.a_age.describe()" + ] + }, + { + "cell_type": "code", + "execution_count": 25, "metadata": {}, "outputs": [ { @@ -532,7 +584,67 @@ "" ] }, - "execution_count": 8, + "execution_count": 25, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "df_contracts.annual_salary.hist(bins=30)" + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "count 7870.000000\n", + "mean 5.916921\n", + "std 6.985214\n", + "min 0.166667\n", + "25% 3.000000\n", + "50% 4.000000\n", + "75% 6.000000\n", + "max 180.000000\n", + "Name: annual_salary, dtype: float64" + ] + }, + "execution_count": 26, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df_contracts.annual_salary.describe()" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 10, "metadata": {}, "output_type": "execute_result" }, @@ -587,66 +699,125 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 41, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "0.026105873821609893" + "1.0 0.987293\n", + "0.0 0.012707\n", + "Name: a_gender, dtype: float64" ] }, - "execution_count": 10, + "execution_count": 41, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "# proportion of female apprentices\n", - "1-(df_contracts.a_gender.sum()/df_contracts.shape[0])" + "df_contracts.a_gender.value_counts(1)" ] }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 28, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "0.023723194861701047" + "9401.0" ] }, - "execution_count": 11, + "execution_count": 28, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "# proportion of female masters\n", - "1-(df_contracts.m_gender.sum()/df_contracts.shape[0])" + "df_contracts.a_gender.sum()" ] }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 42, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "0.7310924369747899" + "9401.0" ] }, - "execution_count": 12, + "execution_count": 42, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "# prop female apprentices with male master\n", - "df_contracts[(df_contracts.a_gender == 0) & (df_contracts.startY < 1800)].m_gender.sum()\\\n", - " /df_contracts[(df_contracts.a_gender == 0) & (df_contracts.startY < 1800)].shape[0]" + "df_contracts.a_gender.sum()" + ] + }, + { + "cell_type": "code", + "execution_count": 44, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "3" + ] + }, + "execution_count": 44, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "sum([True,False,False,True,True])" + ] + }, + { + "cell_type": "code", + "execution_count": 35, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "0.026105873821609893" + ] + }, + "execution_count": 35, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "1-(df_contracts.a_gender.sum()/df_contracts.shape[0])" + ] + }, + { + "cell_type": "code", + "execution_count": 36, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "2.6105873821609893" + ] + }, + "execution_count": 36, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# proportion of female apprentices\n", + "(1-(df_contracts.a_gender.sum()/df_contracts.shape[0]))*100" ] }, { @@ -657,7 +828,7 @@ { "data": { "text/plain": [ - "0.9810528582193992" + "2.3723194861701047" ] }, "execution_count": 13, @@ -665,10 +836,50 @@ "output_type": "execute_result" } ], + "source": [ + "# proportion of female masters\n", + "(1-(df_contracts.m_gender.sum()/df_contracts.shape[0]))*100" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "73.10924369747899" + ] + }, + "execution_count": 14, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# prop female apprentices with male master\n", + "(df_contracts[(df_contracts.a_gender == 0) & (df_contracts.startY < 1800)].m_gender.sum()\\\n", + " /df_contracts[(df_contracts.a_gender == 0) & (df_contracts.startY < 1800)].shape[0])*100" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "98.10528582193993 %\n" + ] + } + ], "source": [ "# prop male apprentices with male master\n", - "df_contracts[(df_contracts.a_gender == 1) & (df_contracts.startY < 1800)].m_gender.sum()\\\n", - " /df_contracts[(df_contracts.a_gender == 1) & (df_contracts.startY < 1800)].shape[0]" + "print((df_contracts[(df_contracts.a_gender == 1) & (df_contracts.startY < 1800)].m_gender.sum()\\\n", + " /df_contracts[(df_contracts.a_gender == 1) & (df_contracts.startY < 1800)].shape[0])*100,\"%\")" ] }, { @@ -680,7 +891,7 @@ }, { "cell_type": "code", - "execution_count": 14, + "execution_count": 46, "metadata": {}, "outputs": [ { @@ -689,7 +900,7 @@ "" ] }, - "execution_count": 14, + "execution_count": 46, "metadata": {}, "output_type": "execute_result" }, @@ -712,7 +923,7 @@ }, { "cell_type": "code", - "execution_count": 15, + "execution_count": 47, "metadata": {}, "outputs": [ { @@ -721,7 +932,7 @@ "" ] }, - "execution_count": 15, + "execution_count": 47, "metadata": {}, "output_type": "execute_result" }, @@ -762,22 +973,22 @@ }, { "cell_type": "code", - "execution_count": 16, + "execution_count": 48, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "" + "" ] }, - "execution_count": 16, + "execution_count": 48, "metadata": {}, "output_type": "execute_result" }, { "data": { - "image/png": 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\n", + "image/png": 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\n", 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" ] @@ -795,7 +1006,7 @@ }, { "cell_type": "code", - "execution_count": 17, + "execution_count": 49, "metadata": {}, "outputs": [ { @@ -804,13 +1015,13 @@ "" ] }, - "execution_count": 17, + "execution_count": 49, "metadata": {}, "output_type": "execute_result" }, { "data": { - "image/png": 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" ] @@ -838,22 +1049,22 @@ }, { "cell_type": "code", - "execution_count": 18, + "execution_count": 50, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "" + "" ] }, - "execution_count": 18, + "execution_count": 50, "metadata": {}, "output_type": "execute_result" }, { "data": { - "image/png": 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\n", 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\n", 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" ] @@ -871,7 +1082,7 @@ }, { "cell_type": "code", - "execution_count": 19, + "execution_count": 51, "metadata": {}, "outputs": [ { @@ -880,13 +1091,13 @@ "" ] }, - "execution_count": 19, + "execution_count": 51, "metadata": {}, "output_type": "execute_result" }, { "data": { - "image/png": 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ofYQQrki6bGa/FA39mqQzddNHDDfBe5R9V/59Sd+I6+b7Eur7W0k/kjSn7G+535G0Ttk3US5E3x8pmP+NqJdzKngXVFK3sj+g70v6MxXd9K9xD7+q7Muh/5L0bvTV04B97JT0/aiP05JeisYbqo+CGp7Sz9+Ea6gelL13+l709YPc391G6yPa/3OSJqKfq3+S9Jl66YN/igwATviXcADghAAGACcEMAA4IYABwAkBDABOCGAAcEIAA4CT/wPJIAmkDAVTggAAAABJRU5ErkJggg==\n", 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" ] @@ -903,22 +1114,22 @@ }, { "cell_type": "code", - "execution_count": 20, + "execution_count": 52, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "" + "" ] }, - "execution_count": 20, + "execution_count": 52, "metadata": {}, "output_type": "execute_result" }, { "data": { - "image/png": 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\n", 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\n", "text/plain": [ "
" ] @@ -989,15 +1200,15 @@ }, { "cell_type": "code", - "execution_count": 21, + "execution_count": 53, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "5.002074395604182\n", - "243.25940618582834\n" + "4.993993145847454\n", + "240.65275836549188\n" ] } ], @@ -1010,15 +1221,35 @@ }, { "cell_type": "code", - "execution_count": 22, + "execution_count": 57, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "4.993993145847454" + ] + }, + "execution_count": 57, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "np.mean(s1)" + ] + }, + { + "cell_type": "code", + "execution_count": 60, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "5.001688993537184\n", - "147.7036196133731\n" + "4.990826061241218\n", + "147.72833254812608\n" ] } ], @@ -1042,7 +1273,7 @@ }, { "cell_type": "code", - "execution_count": 23, + "execution_count": 32, "metadata": {}, "outputs": [ { @@ -1136,7 +1367,7 @@ "max 180.000000 50.000000 15.000000" ] }, - "execution_count": 23, + "execution_count": 32, "metadata": {}, "output_type": "execute_result" } @@ -1173,7 +1404,7 @@ }, { "cell_type": "code", - "execution_count": 24, + "execution_count": 33, "metadata": {}, "outputs": [ { @@ -1232,7 +1463,7 @@ "length -0.361611 -0.430062 1.000000" ] }, - "execution_count": 24, + "execution_count": 33, "metadata": {}, "output_type": "execute_result" } @@ -1243,7 +1474,7 @@ }, { "cell_type": "code", - "execution_count": 25, + "execution_count": 34, "metadata": {}, "outputs": [ { @@ -1252,7 +1483,7 @@ "" ] }, - "execution_count": 25, + "execution_count": 34, "metadata": {}, "output_type": "execute_result" }, @@ -1343,7 +1574,7 @@ }, { "cell_type": "code", - "execution_count": 31, + "execution_count": 2, "metadata": {}, "outputs": [], "source": [ @@ -1354,7 +1585,7 @@ }, { "cell_type": "code", - "execution_count": 32, + "execution_count": 3, "metadata": {}, "outputs": [ { @@ -1434,7 +1665,7 @@ "4 \"@DaveLeeBBC @verge Coal is dying due to nat g... " ] }, - "execution_count": 32, + "execution_count": 3, "metadata": {}, "output_type": "execute_result" } @@ -1445,7 +1676,7 @@ }, { "cell_type": "code", - "execution_count": 33, + "execution_count": 4, "metadata": {}, "outputs": [ { @@ -1454,7 +1685,7 @@ "(2819, 3)" ] }, - "execution_count": 33, + "execution_count": 4, "metadata": {}, "output_type": "execute_result" } @@ -1472,7 +1703,7 @@ }, { "cell_type": "code", - "execution_count": 35, + "execution_count": 5, "metadata": {}, "outputs": [], "source": [ @@ -1485,7 +1716,7 @@ }, { "cell_type": "code", - "execution_count": 36, + "execution_count": 6, "metadata": {}, "outputs": [ { @@ -1503,7 +1734,7 @@ "True" ] }, - "execution_count": 36, + "execution_count": 6, "metadata": {}, "output_type": "execute_result" } @@ -1525,7 +1756,7 @@ }, { "cell_type": "code", - "execution_count": 37, + "execution_count": 7, "metadata": {}, "outputs": [], "source": [ @@ -1535,7 +1766,7 @@ }, { "cell_type": "code", - "execution_count": 38, + "execution_count": 8, "metadata": {}, "outputs": [ { @@ -1553,7 +1784,7 @@ }, { "cell_type": "code", - "execution_count": 39, + "execution_count": 9, "metadata": {}, "outputs": [ { @@ -1581,7 +1812,7 @@ }, { "cell_type": "code", - "execution_count": 40, + "execution_count": 10, "metadata": {}, "outputs": [ { @@ -1590,7 +1821,7 @@ "'!\"#$%&\\'()*+,-./:;<=>?@[\\\\]^_`{|}~'" ] }, - "execution_count": 40, + "execution_count": 10, "metadata": {}, "output_type": "execute_result" } @@ -1601,7 +1832,7 @@ }, { "cell_type": "code", - "execution_count": 41, + "execution_count": 11, "metadata": {}, "outputs": [], "source": [ @@ -1624,7 +1855,7 @@ }, { "cell_type": "code", - "execution_count": 42, + "execution_count": 12, "metadata": {}, "outputs": [ { @@ -1643,7 +1874,7 @@ }, { "cell_type": "code", - "execution_count": 43, + "execution_count": 13, "metadata": {}, "outputs": [], "source": [ @@ -1652,7 +1883,7 @@ }, { "cell_type": "code", - "execution_count": 44, + "execution_count": 14, "metadata": {}, "outputs": [ { @@ -1745,7 +1976,7 @@ "4 @DaveLeeBBC @verge Coal dying fracking It's ba... " ] }, - "execution_count": 44, + "execution_count": 14, "metadata": {}, "output_type": "execute_result" } @@ -1756,7 +1987,7 @@ }, { "cell_type": "code", - "execution_count": 45, + "execution_count": 15, "metadata": {}, "outputs": [], "source": [ @@ -1774,7 +2005,7 @@ }, { "cell_type": "code", - "execution_count": 46, + "execution_count": 16, "metadata": {}, "outputs": [ { @@ -1783,7 +2014,7 @@ "(2819, 7864)" ] }, - "execution_count": 46, + "execution_count": 16, "metadata": {}, "output_type": "execute_result" } @@ -1797,7 +2028,7 @@ }, { "cell_type": "code", - "execution_count": 48, + "execution_count": 17, "metadata": {}, "outputs": [ { @@ -1806,7 +2037,7 @@ "6617" ] }, - "execution_count": 48, + "execution_count": 17, "metadata": {}, "output_type": "execute_result" } @@ -1820,7 +2051,7 @@ }, { "cell_type": "code", - "execution_count": 49, + "execution_count": 18, "metadata": {}, "outputs": [ { @@ -1829,7 +2060,7 @@ "3" ] }, - "execution_count": 49, + "execution_count": 18, "metadata": {}, "output_type": "execute_result" } @@ -1840,7 +2071,7 @@ }, { "cell_type": "code", - "execution_count": 50, + "execution_count": 19, "metadata": {}, "outputs": [ { @@ -1849,7 +2080,7 @@ "3" ] }, - "execution_count": 50, + "execution_count": 19, "metadata": {}, "output_type": "execute_result" } @@ -1870,7 +2101,7 @@ }, { "cell_type": "code", - "execution_count": 51, + "execution_count": 20, "metadata": {}, "outputs": [ { @@ -1888,7 +2119,7 @@ " ('launch', 124)]" ] }, - "execution_count": 51, + "execution_count": 20, "metadata": {}, "output_type": "execute_result" } @@ -1900,7 +2131,7 @@ }, { "cell_type": "code", - "execution_count": 52, + "execution_count": 22, "metadata": {}, "outputs": [ { @@ -1918,13 +2149,12 @@ " ('@BadAstronomer', 11)]" ] }, - "execution_count": 52, + "execution_count": 22, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "dictionary_list_users = sorted(dictionary.items(), key=lambda x:x[1], reverse=True)\n", "[d for d in dictionary_list if d[0].startswith('@')][:10]" ] }, @@ -1941,7 +2171,7 @@ }, { "cell_type": "code", - "execution_count": 53, + "execution_count": 23, "metadata": {}, "outputs": [ { @@ -1954,7 +2184,7 @@ "Name: 0, dtype: object" ] }, - "execution_count": 53, + "execution_count": 23, "metadata": {}, "output_type": "execute_result" } @@ -1967,7 +2197,7 @@ }, { "cell_type": "code", - "execution_count": 54, + "execution_count": 24, "metadata": {}, "outputs": [], "source": [ @@ -1978,7 +2208,7 @@ }, { "cell_type": "code", - "execution_count": 55, + "execution_count": 25, "metadata": {}, "outputs": [ { @@ -1987,7 +2217,7 @@ "3" ] }, - "execution_count": 55, + "execution_count": 25, "metadata": {}, "output_type": "execute_result" } @@ -2001,7 +2231,7 @@ }, { "cell_type": "code", - "execution_count": 56, + "execution_count": 26, "metadata": {}, "outputs": [ { @@ -2039,7 +2269,7 @@ }, { "cell_type": "code", - "execution_count": 57, + "execution_count": 27, "metadata": {}, "outputs": [ { @@ -2048,7 +2278,7 @@ "(2819, 7864)" ] }, - "execution_count": 57, + "execution_count": 27, "metadata": {}, "output_type": "execute_result" } @@ -2062,7 +2292,7 @@ }, { "cell_type": "code", - "execution_count": 58, + "execution_count": 28, "metadata": {}, "outputs": [ { @@ -2071,7 +2301,7 @@ "1.7226760995112569" ] }, - "execution_count": 58, + "execution_count": 28, "metadata": {}, "output_type": "execute_result" } @@ -2082,7 +2312,7 @@ }, { "cell_type": "code", - "execution_count": 59, + "execution_count": 29, "metadata": {}, "outputs": [ { @@ -2091,7 +2321,7 @@ "3" ] }, - "execution_count": 59, + "execution_count": 29, "metadata": {}, "output_type": "execute_result" } @@ -2113,7 +2343,7 @@ }, { "cell_type": "code", - "execution_count": 60, + "execution_count": 30, "metadata": {}, "outputs": [ { @@ -2123,7 +2353,7 @@ "\twith 3 stored elements in Compressed Sparse Row format>" ] }, - "execution_count": 60, + "execution_count": 30, "metadata": {}, "output_type": "execute_result" } @@ -2143,7 +2373,7 @@ }, { "cell_type": "code", - "execution_count": 65, + "execution_count": 31, "metadata": {}, "outputs": [], "source": [ @@ -2166,7 +2396,7 @@ }, { "cell_type": "code", - "execution_count": 66, + "execution_count": 32, "metadata": {}, "outputs": [], "source": [ @@ -2195,7 +2425,7 @@ }, { "cell_type": "code", - "execution_count": 67, + "execution_count": 33, "metadata": {}, "outputs": [ { @@ -2204,7 +2434,7 @@ "['robot', 'spare', 'humanity', 'https://t.co/v7JUJQWfCv']" ] }, - "execution_count": 67, + "execution_count": 33, "metadata": {}, "output_type": "execute_result" } @@ -2215,7 +2445,7 @@ }, { "cell_type": "code", - "execution_count": 68, + "execution_count": 34, "metadata": {}, "outputs": [ { @@ -2224,7 +2454,7 @@ "['NOUN', 'VERB', 'NOUN', 'NOUN']" ] }, - "execution_count": 68, + "execution_count": 34, "metadata": {}, "output_type": "execute_result" } @@ -2235,7 +2465,7 @@ }, { "cell_type": "code", - "execution_count": 69, + "execution_count": 35, "metadata": {}, "outputs": [ { @@ -2244,7 +2474,7 @@ "[https://t.co/v7JUJQWfCv]" ] }, - "execution_count": 69, + "execution_count": 35, "metadata": {}, "output_type": "execute_result" } @@ -2255,7 +2485,7 @@ }, { "cell_type": "code", - "execution_count": 70, + "execution_count": 36, "metadata": {}, "outputs": [ { @@ -2264,7 +2494,7 @@ "[Shortville]" ] }, - "execution_count": 70, + "execution_count": 36, "metadata": {}, "output_type": "execute_result" } @@ -2293,7 +2523,7 @@ }, { "cell_type": "code", - "execution_count": 71, + "execution_count": 37, "metadata": {}, "outputs": [ { @@ -2311,7 +2541,7 @@ }, { "cell_type": "code", - "execution_count": 72, + "execution_count": 38, "metadata": {}, "outputs": [], "source": [ @@ -2320,7 +2550,7 @@ }, { "cell_type": "code", - "execution_count": 73, + "execution_count": 39, "metadata": {}, "outputs": [ { @@ -2339,7 +2569,7 @@ }, { "cell_type": "code", - "execution_count": 74, + "execution_count": 40, "metadata": {}, "outputs": [], "source": [ @@ -2348,7 +2578,7 @@ }, { "cell_type": "code", - "execution_count": 75, + "execution_count": 41, "metadata": {}, "outputs": [ { @@ -2358,7 +2588,7 @@ "\twith 16 stored elements in Compressed Sparse Row format>" ] }, - "execution_count": 75, + "execution_count": 41, "metadata": {}, "output_type": "execute_result" } @@ -2369,7 +2599,7 @@ }, { "cell_type": "code", - "execution_count": 76, + "execution_count": 45, "metadata": {}, "outputs": [ { @@ -2401,7 +2631,7 @@ }, { "cell_type": "code", - "execution_count": 77, + "execution_count": 46, "metadata": {}, "outputs": [ { @@ -2416,7 +2646,7 @@ " 0.640603 ]))" ] }, - "execution_count": 77, + "execution_count": 46, "metadata": {}, "output_type": "execute_result" } @@ -2429,7 +2659,7 @@ }, { "cell_type": "code", - "execution_count": 78, + "execution_count": 50, "metadata": {}, "outputs": [], "source": [ @@ -2438,7 +2668,7 @@ }, { "cell_type": "code", - "execution_count": 79, + "execution_count": 51, "metadata": {}, "outputs": [ { @@ -2478,7 +2708,52 @@ "\n", "* Can you rank the matched tweets using their tf-idf weights, so to put higher weighted tweets first?\n", "* Which limitations do you think a bag of words representation has?\n", - "* Can you spot any limitations of this approach based on similarity measures over bag of words representations?" + "* Can you spot any limitations of this approach based on similarity measures over bag of words representations?\n", + "\n", + "#### Exercises\n", + "\n", + "* Find the highest IDF tokens in the corpus and discuss your results.\n", + "* Find the most frequent named entities and discuss your results.\n", + "* Are there trends in the topics of tweets during the day? And over time?\n", + "* Perform a **sentiment analysis** of the corpus, see here https://spacy.io/universe/project/spacy-textblob" + ] + }, + { + "cell_type": "code", + "execution_count": 55, + "metadata": {}, + "outputs": [], + "source": [ + "#!pip install spacytextblob\n", + "#!python -m spacy download en_core_web_sm\n", + "#!python -m textblob.download_corpora" + ] + }, + { + "cell_type": "code", + "execution_count": 56, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "-0.125" + ] + }, + "execution_count": 56, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "import spacy\n", + "from spacytextblob.spacytextblob import SpacyTextBlob\n", + "\n", + "nlp = spacy.load('en_core_web_sm')\n", + "nlp.add_pipe('spacytextblob')\n", + "text = 'I had a really horrible day. It was the worst day ever! But every now and then I have a really good day that makes me happy.'\n", + "doc = nlp(text)\n", + "doc._.blob.polarity " ] }, {