From 8c6d84c829f3c8e89b49c65395ab6f024020a149 Mon Sep 17 00:00:00 2001 From: Paola Serrano Date: Sun, 9 Oct 2022 22:50:10 -0500 Subject: [PATCH] Paola Serrano List Comprehension Delivery --- .../.ipynb_checkpoints/main-checkpoint.ipynb | 250 +++++++++++++++--- .../your-code/Learning.ipynb | 2 +- lab-list-comprehensions/your-code/main.ipynb | 250 +++++++++++++++--- 3 files changed, 433 insertions(+), 69 deletions(-) diff --git a/lab-list-comprehensions/your-code/.ipynb_checkpoints/main-checkpoint.ipynb b/lab-list-comprehensions/your-code/.ipynb_checkpoints/main-checkpoint.ipynb index 9860215..effb1e8 100644 --- a/lab-list-comprehensions/your-code/.ipynb_checkpoints/main-checkpoint.ipynb +++ b/lab-list-comprehensions/your-code/.ipynb_checkpoints/main-checkpoint.ipynb @@ -11,7 +11,7 @@ }, { "cell_type": "code", - "execution_count": 1, + "execution_count": 44, "metadata": {}, "outputs": [], "source": [ @@ -29,10 +29,21 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 45, "metadata": {}, - "outputs": [], - "source": [] + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50]\n" + ] + } + ], + "source": [ + "lst = [i for i in range(51)]\n", + "print(lst)" + ] }, { "cell_type": "markdown", @@ -43,10 +54,21 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 46, "metadata": {}, - "outputs": [], - "source": [] + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[0, 2, 4, 6, 8, 10, 12, 14, 16, 18, 20, 22, 24, 26, 28, 30, 32, 34, 36, 38, 40, 42, 44, 46, 48, 50, 52, 54, 56, 58, 60, 62, 64, 66, 68, 70, 72, 74, 76, 78, 80, 82, 84, 86, 88, 90, 92, 94, 96, 98, 100, 102, 104, 106, 108, 110, 112, 114, 116, 118, 120, 122, 124, 126, 128, 130, 132, 134, 136, 138, 140, 142, 144, 146, 148, 150, 152, 154, 156, 158, 160, 162, 164, 166, 168, 170, 172, 174, 176, 178, 180, 182, 184, 186, 188, 190, 192, 194, 196, 198, 200]\n" + ] + } + ], + "source": [ + "lst = [i for i in range(201)if i %2 ==0]\n", + "print(lst)" + ] }, { "cell_type": "markdown", @@ -57,7 +79,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 9, "metadata": {}, "outputs": [], "source": [ @@ -75,10 +97,21 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 47, "metadata": {}, - "outputs": [], - "source": [] + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[0.84062117, 0.48006452, 0.7876326, 0.77109654, 0.44409793, 0.09014516, 0.81835917, 0.87645456, 0.7066597, 0.09610873, 0.41247947, 0.57433389, 0.29960807, 0.42315023, 0.34452557, 0.4751035, 0.17003563, 0.46843998, 0.92796258, 0.69814654, 0.41290051, 0.19561071, 0.16284783, 0.97016248, 0.71725408, 0.87702738, 0.31244595, 0.76615487, 0.20754036, 0.57871812, 0.07214068, 0.40356048, 0.12149553, 0.53222417, 0.9976855, 0.12536346, 0.80930099, 0.50962849, 0.94555126, 0.33364763]\n" + ] + } + ], + "source": [ + "list_mat = [number for n in a for number in n]\n", + "print (list_mat)" + ] }, { "cell_type": "markdown", @@ -89,10 +122,23 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 48, "metadata": {}, - "outputs": [], - "source": [] + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[0.84062117, 0.7876326, 0.77109654, 0.81835917, 0.87645456, 0.7066597, 0.57433389, 0.92796258, 0.69814654, 0.97016248, 0.71725408, 0.87702738, 0.76615487, 0.57871812, 0.53222417, 0.9976855, 0.80930099, 0.50962849, 0.94555126]\n" + ] + } + ], + "source": [ + "list_mat_0_5 = [number for n in a for number in n if number >=0.5]\n", + "print (list_mat_0_5)\n", + "\n", + "#Qué pasa si quiero meter un else" + ] }, { "cell_type": "markdown", @@ -103,7 +149,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 24, "metadata": {}, "outputs": [], "source": [ @@ -125,10 +171,21 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 49, "metadata": {}, - "outputs": [], - "source": [] + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[0.55867166, 0.06210792, 0.08147297, 0.82579068, 0.91512478, 0.06833034, 0.05440634, 0.65857693, 0.30296619, 0.06769833, 0.96031863, 0.51293743, 0.09143215, 0.71893382, 0.45850679, 0.58256464, 0.59005654, 0.56266457, 0.71600294, 0.87392666, 0.11434044, 0.8694668, 0.65669313, 0.10708681, 0.07529684, 0.46470767, 0.47984544, 0.65368638, 0.14901286, 0.23760688]\n" + ] + } + ], + "source": [ + "list_mat6 = [number for list1 in b for list2 in list1 for number in list2]\n", + "print(list_mat6)" + ] }, { "cell_type": "markdown", @@ -139,10 +196,21 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 28, "metadata": {}, - "outputs": [], - "source": [] + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[0.48006452, 0.44409793, 0.09014516, 0.09610873, 0.41247947, 0.29960807, 0.42315023, 0.34452557, 0.4751035, 0.17003563, 0.46843998, 0.41290051, 0.19561071, 0.16284783, 0.31244595, 0.20754036, 0.07214068, 0.40356048, 0.12149553, 0.12536346, 0.33364763]\n" + ] + } + ], + "source": [ + "list_mate = [number for n in a for number in n if number <=0.5]\n", + "print (list_mate)\n" + ] }, { "cell_type": "markdown", @@ -153,10 +221,42 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 50, "metadata": {}, - "outputs": [], - "source": [] + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " 0 1 2 3 4 5 6 \\\n", + "0 0.734751 0.195362 0.734309 0.598184 0.763433 0.263434 0.868066 \n", + "1 0.772607 0.445391 0.249642 0.787922 0.598583 0.827238 0.624126 \n", + "2 0.226428 0.268764 0.694262 0.622335 0.063843 0.122683 0.815625 \n", + "3 0.362748 0.495430 0.113876 0.594149 0.612522 0.625204 0.864050 \n", + "4 0.033415 0.340433 0.464971 0.363737 0.025815 0.434129 0.415163 \n", + "\n", + " 7 8 9 10 11 12 13 \\\n", + "0 0.058092 0.753502 0.587513 0.311608 0.178356 0.182922 0.147631 \n", + "1 0.601524 0.688753 0.338870 0.081595 0.471474 0.267443 0.453351 \n", + "2 0.584542 0.032594 0.589775 0.764350 0.650973 0.565705 0.691784 \n", + "3 0.260279 0.528873 0.168043 0.715929 0.677014 0.175735 0.632370 \n", + "4 0.892210 0.381701 0.415264 0.790801 0.696930 0.819751 0.944029 \n", + "\n", + " 14 15 16 17 18 19 \n", + "0 0.391188 0.816049 0.749068 0.293260 0.937828 0.880858 \n", + "1 0.800716 0.045749 0.683793 0.389789 0.016787 0.503695 \n", + "2 0.265223 0.739031 0.560394 0.334802 0.517694 0.646110 \n", + "3 0.926715 0.085675 0.120525 0.141746 0.771144 0.489660 \n", + "4 0.869965 0.041723 0.819140 0.676051 0.109349 0.872947 \n" + ] + } + ], + "source": [ + "rutas=['../data/sample_file_0.txt']\n", + "dfs=[pd.read_csv(ruta) for ruta in rutas]\n", + "data=pd.concat(dfs)\n", + "print(data)" + ] }, { "cell_type": "markdown", @@ -167,10 +267,44 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 54, "metadata": {}, - "outputs": [], - "source": [] + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " 0 1 2 3 4 5 6 \\\n", + "0 0.734751 0.195362 0.734309 0.598184 0.763433 0.263434 0.868066 \n", + "1 0.772607 0.445391 0.249642 0.787922 0.598583 0.827238 0.624126 \n", + "2 0.226428 0.268764 0.694262 0.622335 0.063843 0.122683 0.815625 \n", + "3 0.362748 0.495430 0.113876 0.594149 0.612522 0.625204 0.864050 \n", + "4 0.033415 0.340433 0.464971 0.363737 0.025815 0.434129 0.415163 \n", + "\n", + " 7 8 9 10 11 12 13 \\\n", + "0 0.058092 0.753502 0.587513 0.311608 0.178356 0.182922 0.147631 \n", + "1 0.601524 0.688753 0.338870 0.081595 0.471474 0.267443 0.453351 \n", + "2 0.584542 0.032594 0.589775 0.764350 0.650973 0.565705 0.691784 \n", + "3 0.260279 0.528873 0.168043 0.715929 0.677014 0.175735 0.632370 \n", + "4 0.892210 0.381701 0.415264 0.790801 0.696930 0.819751 0.944029 \n", + "\n", + " 14 15 16 17 18 19 \n", + "0 0.391188 0.816049 0.749068 0.293260 0.937828 0.880858 \n", + "1 0.800716 0.045749 0.683793 0.389789 0.016787 0.503695 \n", + "2 0.265223 0.739031 0.560394 0.334802 0.517694 0.646110 \n", + "3 0.926715 0.085675 0.120525 0.141746 0.771144 0.489660 \n", + "4 0.869965 0.041723 0.819140 0.676051 0.109349 0.872947 \n" + ] + } + ], + "source": [ + "dfs=[pd.read_csv(ruta) for ruta in rutas]\n", + "data = pd.concat(dfs)\n", + "columns =data.head(10)\n", + "print(columns)\n", + "\n", + "#aiuda" + ] }, { "cell_type": "markdown", @@ -179,6 +313,29 @@ "### 8. Use a list comprehension to select and print the column numbers for columns from the data set whose median is less than 0.48." ] }, + { + "cell_type": "code", + "execution_count": 64, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "['3', '4', '6', '7', '8', '10', '11', '13', '14', '16', '18', '19']\n" + ] + } + ], + "source": [ + "median = [column for column, values in data.items() if values.median() >= 0.48]\n", + "print(median)\n", + "\n", + "#medians = data.median\n", + "#print(medians)\n", + "\n", + "#cómo podría ahí meter un if" + ] + }, { "cell_type": "code", "execution_count": null, @@ -195,10 +352,24 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 69, "metadata": {}, - "outputs": [], - "source": [] + "outputs": [ + { + "ename": "SyntaxError", + "evalue": "invalid syntax (, line 1)", + "output_type": "error", + "traceback": [ + "\u001b[0;36m File \u001b[0;32m\"\"\u001b[0;36m, line \u001b[0;32m1\u001b[0m\n\u001b[0;31m dfs[20]= [19 if s<0.1]\u001b[0m\n\u001b[0m ^\u001b[0m\n\u001b[0;31mSyntaxError\u001b[0m\u001b[0;31m:\u001b[0m invalid syntax\n" + ] + } + ], + "source": [ + "dfs[20]= [19 if s<0.1]\n", + "print(dfs)\n", + "\n", + "#¿Podemos checarlo en clase? " + ] }, { "cell_type": "markdown", @@ -209,10 +380,21 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 66, "metadata": {}, - "outputs": [], - "source": [] + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[0.7726065618884856, 0.7879222598307901, 0.7634334207035142, 0.8272382506031565, 0.8680663549990865, 0.8156248348531779, 0.8640498341918672, 0.8922104131249807, 0.7535023641545986, 0.7643495353716243, 0.7908008486938785, 0.8197511267505602, 0.944028613970866, 0.8007161479739965, 0.9267149333179154, 0.8699647196644326, 0.8160493911980281, 0.8191396818327873, 0.93782845947314, 0.7711443458405843, 0.8808575048234396, 0.8729470926088846]\n" + ] + } + ], + "source": [ + "data_range = [value for column, values in data.items() for value in values if value >= 0.7 and value >= 0.75]\n", + "print(data_range)" + ] } ], "metadata": { @@ -231,7 +413,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.7.7" + "version": "3.8.5" } }, "nbformat": 4, diff --git a/lab-list-comprehensions/your-code/Learning.ipynb b/lab-list-comprehensions/your-code/Learning.ipynb index a1be3df..152d15a 100644 --- a/lab-list-comprehensions/your-code/Learning.ipynb +++ b/lab-list-comprehensions/your-code/Learning.ipynb @@ -287,7 +287,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.7.7" + "version": "3.8.5" } }, "nbformat": 4, diff --git a/lab-list-comprehensions/your-code/main.ipynb b/lab-list-comprehensions/your-code/main.ipynb index 9860215..effb1e8 100644 --- a/lab-list-comprehensions/your-code/main.ipynb +++ b/lab-list-comprehensions/your-code/main.ipynb @@ -11,7 +11,7 @@ }, { "cell_type": "code", - "execution_count": 1, + "execution_count": 44, "metadata": {}, "outputs": [], "source": [ @@ -29,10 +29,21 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 45, "metadata": {}, - "outputs": [], - "source": [] + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50]\n" + ] + } + ], + "source": [ + "lst = [i for i in range(51)]\n", + "print(lst)" + ] }, { "cell_type": "markdown", @@ -43,10 +54,21 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 46, "metadata": {}, - "outputs": [], - "source": [] + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[0, 2, 4, 6, 8, 10, 12, 14, 16, 18, 20, 22, 24, 26, 28, 30, 32, 34, 36, 38, 40, 42, 44, 46, 48, 50, 52, 54, 56, 58, 60, 62, 64, 66, 68, 70, 72, 74, 76, 78, 80, 82, 84, 86, 88, 90, 92, 94, 96, 98, 100, 102, 104, 106, 108, 110, 112, 114, 116, 118, 120, 122, 124, 126, 128, 130, 132, 134, 136, 138, 140, 142, 144, 146, 148, 150, 152, 154, 156, 158, 160, 162, 164, 166, 168, 170, 172, 174, 176, 178, 180, 182, 184, 186, 188, 190, 192, 194, 196, 198, 200]\n" + ] + } + ], + "source": [ + "lst = [i for i in range(201)if i %2 ==0]\n", + "print(lst)" + ] }, { "cell_type": "markdown", @@ -57,7 +79,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 9, "metadata": {}, "outputs": [], "source": [ @@ -75,10 +97,21 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 47, "metadata": {}, - "outputs": [], - "source": [] + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[0.84062117, 0.48006452, 0.7876326, 0.77109654, 0.44409793, 0.09014516, 0.81835917, 0.87645456, 0.7066597, 0.09610873, 0.41247947, 0.57433389, 0.29960807, 0.42315023, 0.34452557, 0.4751035, 0.17003563, 0.46843998, 0.92796258, 0.69814654, 0.41290051, 0.19561071, 0.16284783, 0.97016248, 0.71725408, 0.87702738, 0.31244595, 0.76615487, 0.20754036, 0.57871812, 0.07214068, 0.40356048, 0.12149553, 0.53222417, 0.9976855, 0.12536346, 0.80930099, 0.50962849, 0.94555126, 0.33364763]\n" + ] + } + ], + "source": [ + "list_mat = [number for n in a for number in n]\n", + "print (list_mat)" + ] }, { "cell_type": "markdown", @@ -89,10 +122,23 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 48, "metadata": {}, - "outputs": [], - "source": [] + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[0.84062117, 0.7876326, 0.77109654, 0.81835917, 0.87645456, 0.7066597, 0.57433389, 0.92796258, 0.69814654, 0.97016248, 0.71725408, 0.87702738, 0.76615487, 0.57871812, 0.53222417, 0.9976855, 0.80930099, 0.50962849, 0.94555126]\n" + ] + } + ], + "source": [ + "list_mat_0_5 = [number for n in a for number in n if number >=0.5]\n", + "print (list_mat_0_5)\n", + "\n", + "#Qué pasa si quiero meter un else" + ] }, { "cell_type": "markdown", @@ -103,7 +149,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 24, "metadata": {}, "outputs": [], "source": [ @@ -125,10 +171,21 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 49, "metadata": {}, - "outputs": [], - "source": [] + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[0.55867166, 0.06210792, 0.08147297, 0.82579068, 0.91512478, 0.06833034, 0.05440634, 0.65857693, 0.30296619, 0.06769833, 0.96031863, 0.51293743, 0.09143215, 0.71893382, 0.45850679, 0.58256464, 0.59005654, 0.56266457, 0.71600294, 0.87392666, 0.11434044, 0.8694668, 0.65669313, 0.10708681, 0.07529684, 0.46470767, 0.47984544, 0.65368638, 0.14901286, 0.23760688]\n" + ] + } + ], + "source": [ + "list_mat6 = [number for list1 in b for list2 in list1 for number in list2]\n", + "print(list_mat6)" + ] }, { "cell_type": "markdown", @@ -139,10 +196,21 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 28, "metadata": {}, - "outputs": [], - "source": [] + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[0.48006452, 0.44409793, 0.09014516, 0.09610873, 0.41247947, 0.29960807, 0.42315023, 0.34452557, 0.4751035, 0.17003563, 0.46843998, 0.41290051, 0.19561071, 0.16284783, 0.31244595, 0.20754036, 0.07214068, 0.40356048, 0.12149553, 0.12536346, 0.33364763]\n" + ] + } + ], + "source": [ + "list_mate = [number for n in a for number in n if number <=0.5]\n", + "print (list_mate)\n" + ] }, { "cell_type": "markdown", @@ -153,10 +221,42 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 50, "metadata": {}, - "outputs": [], - "source": [] + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " 0 1 2 3 4 5 6 \\\n", + "0 0.734751 0.195362 0.734309 0.598184 0.763433 0.263434 0.868066 \n", + "1 0.772607 0.445391 0.249642 0.787922 0.598583 0.827238 0.624126 \n", + "2 0.226428 0.268764 0.694262 0.622335 0.063843 0.122683 0.815625 \n", + "3 0.362748 0.495430 0.113876 0.594149 0.612522 0.625204 0.864050 \n", + "4 0.033415 0.340433 0.464971 0.363737 0.025815 0.434129 0.415163 \n", + "\n", + " 7 8 9 10 11 12 13 \\\n", + "0 0.058092 0.753502 0.587513 0.311608 0.178356 0.182922 0.147631 \n", + "1 0.601524 0.688753 0.338870 0.081595 0.471474 0.267443 0.453351 \n", + "2 0.584542 0.032594 0.589775 0.764350 0.650973 0.565705 0.691784 \n", + "3 0.260279 0.528873 0.168043 0.715929 0.677014 0.175735 0.632370 \n", + "4 0.892210 0.381701 0.415264 0.790801 0.696930 0.819751 0.944029 \n", + "\n", + " 14 15 16 17 18 19 \n", + "0 0.391188 0.816049 0.749068 0.293260 0.937828 0.880858 \n", + "1 0.800716 0.045749 0.683793 0.389789 0.016787 0.503695 \n", + "2 0.265223 0.739031 0.560394 0.334802 0.517694 0.646110 \n", + "3 0.926715 0.085675 0.120525 0.141746 0.771144 0.489660 \n", + "4 0.869965 0.041723 0.819140 0.676051 0.109349 0.872947 \n" + ] + } + ], + "source": [ + "rutas=['../data/sample_file_0.txt']\n", + "dfs=[pd.read_csv(ruta) for ruta in rutas]\n", + "data=pd.concat(dfs)\n", + "print(data)" + ] }, { "cell_type": "markdown", @@ -167,10 +267,44 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 54, "metadata": {}, - "outputs": [], - "source": [] + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " 0 1 2 3 4 5 6 \\\n", + "0 0.734751 0.195362 0.734309 0.598184 0.763433 0.263434 0.868066 \n", + "1 0.772607 0.445391 0.249642 0.787922 0.598583 0.827238 0.624126 \n", + "2 0.226428 0.268764 0.694262 0.622335 0.063843 0.122683 0.815625 \n", + "3 0.362748 0.495430 0.113876 0.594149 0.612522 0.625204 0.864050 \n", + "4 0.033415 0.340433 0.464971 0.363737 0.025815 0.434129 0.415163 \n", + "\n", + " 7 8 9 10 11 12 13 \\\n", + "0 0.058092 0.753502 0.587513 0.311608 0.178356 0.182922 0.147631 \n", + "1 0.601524 0.688753 0.338870 0.081595 0.471474 0.267443 0.453351 \n", + "2 0.584542 0.032594 0.589775 0.764350 0.650973 0.565705 0.691784 \n", + "3 0.260279 0.528873 0.168043 0.715929 0.677014 0.175735 0.632370 \n", + "4 0.892210 0.381701 0.415264 0.790801 0.696930 0.819751 0.944029 \n", + "\n", + " 14 15 16 17 18 19 \n", + "0 0.391188 0.816049 0.749068 0.293260 0.937828 0.880858 \n", + "1 0.800716 0.045749 0.683793 0.389789 0.016787 0.503695 \n", + "2 0.265223 0.739031 0.560394 0.334802 0.517694 0.646110 \n", + "3 0.926715 0.085675 0.120525 0.141746 0.771144 0.489660 \n", + "4 0.869965 0.041723 0.819140 0.676051 0.109349 0.872947 \n" + ] + } + ], + "source": [ + "dfs=[pd.read_csv(ruta) for ruta in rutas]\n", + "data = pd.concat(dfs)\n", + "columns =data.head(10)\n", + "print(columns)\n", + "\n", + "#aiuda" + ] }, { "cell_type": "markdown", @@ -179,6 +313,29 @@ "### 8. Use a list comprehension to select and print the column numbers for columns from the data set whose median is less than 0.48." ] }, + { + "cell_type": "code", + "execution_count": 64, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "['3', '4', '6', '7', '8', '10', '11', '13', '14', '16', '18', '19']\n" + ] + } + ], + "source": [ + "median = [column for column, values in data.items() if values.median() >= 0.48]\n", + "print(median)\n", + "\n", + "#medians = data.median\n", + "#print(medians)\n", + "\n", + "#cómo podría ahí meter un if" + ] + }, { "cell_type": "code", "execution_count": null, @@ -195,10 +352,24 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 69, "metadata": {}, - "outputs": [], - "source": [] + "outputs": [ + { + "ename": "SyntaxError", + "evalue": "invalid syntax (, line 1)", + "output_type": "error", + "traceback": [ + "\u001b[0;36m File \u001b[0;32m\"\"\u001b[0;36m, line \u001b[0;32m1\u001b[0m\n\u001b[0;31m dfs[20]= [19 if s<0.1]\u001b[0m\n\u001b[0m ^\u001b[0m\n\u001b[0;31mSyntaxError\u001b[0m\u001b[0;31m:\u001b[0m invalid syntax\n" + ] + } + ], + "source": [ + "dfs[20]= [19 if s<0.1]\n", + "print(dfs)\n", + "\n", + "#¿Podemos checarlo en clase? " + ] }, { "cell_type": "markdown", @@ -209,10 +380,21 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 66, "metadata": {}, - "outputs": [], - "source": [] + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[0.7726065618884856, 0.7879222598307901, 0.7634334207035142, 0.8272382506031565, 0.8680663549990865, 0.8156248348531779, 0.8640498341918672, 0.8922104131249807, 0.7535023641545986, 0.7643495353716243, 0.7908008486938785, 0.8197511267505602, 0.944028613970866, 0.8007161479739965, 0.9267149333179154, 0.8699647196644326, 0.8160493911980281, 0.8191396818327873, 0.93782845947314, 0.7711443458405843, 0.8808575048234396, 0.8729470926088846]\n" + ] + } + ], + "source": [ + "data_range = [value for column, values in data.items() for value in values if value >= 0.7 and value >= 0.75]\n", + "print(data_range)" + ] } ], "metadata": { @@ -231,7 +413,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.7.7" + "version": "3.8.5" } }, "nbformat": 4,