diff --git a/AIandML/e1_python_basics/e1.3_pandas.ipynb b/AIandML/e1_python_basics/e1.3_pandas.ipynb index 5bb0eb7..66993ec 100644 --- a/AIandML/e1_python_basics/e1.3_pandas.ipynb +++ b/AIandML/e1_python_basics/e1.3_pandas.ipynb @@ -9,7 +9,7 @@ "\n", "|学号 |姓名 |\n", "|----------|--------|\n", - "| | |\n", + "|***REMOVED***|***REMOVED***|\n", "\n", "本部分的实验，需要自己在网络学习相关基础函数使用。" ] @@ -23,7 +23,7 @@ }, { "cell_type": "code", - "execution_count": 1, + "execution_count": 3, "metadata": {}, "outputs": [], "source": [ @@ -44,7 +44,7 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 4, "metadata": {}, "outputs": [], "source": [ @@ -60,20 +60,293 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 9, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/html": [ + "
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PassengerIdSurvivedPclassNameSexAgeSibSpParchTicketFareCabinEmbarked
88688702Montvila, Rev. Juozasmale27.00021153613.00NaNS
88788811Graham, Miss. Margaret Edithfemale19.00011205330.00B42S
88888903Johnston, Miss. Catherine Helen \"Carrie\"femaleNaN12W./C. 660723.45NaNS
88989011Behr, Mr. Karl Howellmale26.00011136930.00C148C
89089103Dooley, Mr. Patrickmale32.0003703767.75NaNQ
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" + ], + "text/plain": [ + " PassengerId Survived Pclass Name \\\n", + "886 887 0 2 Montvila, Rev. Juozas \n", + "887 888 1 1 Graham, Miss. Margaret Edith \n", + "888 889 0 3 Johnston, Miss. Catherine Helen \"Carrie\" \n", + "889 890 1 1 Behr, Mr. Karl Howell \n", + "890 891 0 3 Dooley, Mr. Patrick \n", + "\n", + " Sex Age SibSp Parch Ticket Fare Cabin Embarked \n", + "886 male 27.0 0 0 211536 13.00 NaN S \n", + "887 female 19.0 0 0 112053 30.00 B42 S \n", + "888 female NaN 1 2 W./C. 6607 23.45 NaN S \n", + "889 male 26.0 0 0 111369 30.00 C148 C \n", + "890 male 32.0 0 0 370376 7.75 NaN Q " + ] + }, + "execution_count": 9, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ - "# 请填写相应代码\n" + "titanic.head(5)\n", + "\n" ] }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 10, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/html": [ + "
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PassengerIdSurvivedPclassNameSexAgeSibSpParchTicketFareCabinEmbarked
88688702Montvila, Rev. Juozasmale27.00021153613.00NaNS
88788811Graham, Miss. Margaret Edithfemale19.00011205330.00B42S
88888903Johnston, Miss. Catherine Helen \"Carrie\"femaleNaN12W./C. 660723.45NaNS
88989011Behr, Mr. Karl Howellmale26.00011136930.00C148C
89089103Dooley, Mr. Patrickmale32.0003703767.75NaNQ
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" + ], + "text/plain": [ + " PassengerId Survived Pclass Name \\\n", + "886 887 0 2 Montvila, Rev. Juozas \n", + "887 888 1 1 Graham, Miss. Margaret Edith \n", + "888 889 0 3 Johnston, Miss. Catherine Helen \"Carrie\" \n", + "889 890 1 1 Behr, Mr. Karl Howell \n", + "890 891 0 3 Dooley, Mr. Patrick \n", + "\n", + " Sex Age SibSp Parch Ticket Fare Cabin Embarked \n", + "886 male 27.0 0 0 211536 13.00 NaN S \n", + "887 female 19.0 0 0 112053 30.00 B42 S \n", + "888 female NaN 1 2 W./C. 6607 23.45 NaN S \n", + "889 male 26.0 0 0 111369 30.00 C148 C \n", + "890 male 32.0 0 0 370376 7.75 NaN Q " + ] + }, + "execution_count": 10, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ - "# 请填写相应代码\n" + "titanic.tail(5)" ] }, { @@ -85,11 +358,20 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 12, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "(891, 12)\n" + ] + } + ], "source": [ - "# 请填写相应代码\n" + "print(titanic.shape)\n", + "\n" ] }, { @@ -101,7 +383,7 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 13, "metadata": {}, "outputs": [ { @@ -358,7 +640,7 @@ "[891 rows x 11 columns]" ] }, - "execution_count": 6, + "execution_count": 13, "metadata": {}, "output_type": "execute_result" } @@ -376,11 +658,34 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 14, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "PassengerId False\n", + "Survived False\n", + "Pclass False\n", + "Name False\n", + "Sex False\n", + "Age True\n", + "SibSp False\n", + "Parch False\n", + "Ticket False\n", + "Fare False\n", + "Cabin True\n", + "Embarked True\n", + "dtype: bool" + ] + }, + "execution_count": 14, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ - "# 请填写相应代码\n" + "titanic.isnull().any()\n" ] }, { @@ -392,20 +697,37 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 15, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "80.0\n", + "0.42\n" + ] + } + ], "source": [ - "# 请填写相应代码\n" + "print(titanic['Age'].max())" ] }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 16, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "0.42\n" + ] + } + ], "source": [ - "# 请填写相应代码\n" + "print(titanic['Age'].min())\n" ] }, { @@ -417,11 +739,21 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 17, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "342\n" + ] + } + ], "source": [ - "# 请填写相应代码\n" + "survived = titanic[titanic['Survived'] == 1]\n", + "\n", + "print(survived.shape[0])\n" ] }, { @@ -433,20 +765,46 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 24, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "male:0.18890814558058924\n", + "female: 0.7420382165605095\n" + ] + } + ], "source": [ - "# 请填写相应代码\n" + "male=titanic[titanic['Sex']=='male']\n", + "survived=male[male['Survived'] == 1]\n", + "print(\"male:\" ,end=\"\")\n", + "print(survived.shape[0]/male.shape[0])\n", + "\n", + "\n" ] }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 25, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "female: 0.7420382165605095\n" + ] + } + ], "source": [ - "# 请填写相应代码\n" + "female=titanic[titanic['Sex']=='female']\n", + "survived=female[female['Survived'] == 1]\n", + "print(\"female: \",end=\"\")\n", + "print(survived.shape[0]/female.shape[0])\n", + "\n" ] }, { @@ -458,11 +816,252 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": 19, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/html": [ + "
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PassengerIdSurvivedPclassNameSexAgeSibSpParchTicketFareCabinEmbarked
25825911Ward, Miss. Annafemale35.000PC 17755512.3292NaNC
73773811Lesurer, Mr. Gustave Jmale35.000PC 17755512.3292B101C
67968011Cardeza, Mr. Thomas Drake Martinezmale36.001PC 17755512.3292B51 B53 B55C
888911Fortune, Miss. Mabel Helenfemale23.03219950263.0000C23 C25 C27S
272801Fortune, Mr. Charles Alexandermale19.03219950263.0000C23 C25 C27S
.......................................
63363401Parr, Mr. William Henry MarshmaleNaN001120520.0000NaNS
41341402Cunningham, Mr. Alfred FlemingmaleNaN002398530.0000NaNS
82282301Reuchlin, Jonkheer. John Georgemale38.000199720.0000NaNS
73273302Knight, Mr. Robert JmaleNaN002398550.0000NaNS
67467502Watson, Mr. Ennis HastingsmaleNaN002398560.0000NaNS
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891 rows × 12 columns

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" + ], + "text/plain": [ + " PassengerId Survived Pclass Name \\\n", + "258 259 1 1 Ward, Miss. Anna \n", + "737 738 1 1 Lesurer, Mr. Gustave J \n", + "679 680 1 1 Cardeza, Mr. Thomas Drake Martinez \n", + "88 89 1 1 Fortune, Miss. Mabel Helen \n", + "27 28 0 1 Fortune, Mr. Charles Alexander \n", + ".. ... ... ... ... \n", + "633 634 0 1 Parr, Mr. William Henry Marsh \n", + "413 414 0 2 Cunningham, Mr. Alfred Fleming \n", + "822 823 0 1 Reuchlin, Jonkheer. John George \n", + "732 733 0 2 Knight, Mr. Robert J \n", + "674 675 0 2 Watson, Mr. Ennis Hastings \n", + "\n", + " Sex Age SibSp Parch Ticket Fare Cabin Embarked \n", + "258 female 35.0 0 0 PC 17755 512.3292 NaN C \n", + "737 male 35.0 0 0 PC 17755 512.3292 B101 C \n", + "679 male 36.0 0 1 PC 17755 512.3292 B51 B53 B55 C \n", + "88 female 23.0 3 2 19950 263.0000 C23 C25 C27 S \n", + "27 male 19.0 3 2 19950 263.0000 C23 C25 C27 S \n", + ".. ... ... ... ... ... ... ... ... \n", + "633 male NaN 0 0 112052 0.0000 NaN S \n", + "413 male NaN 0 0 239853 0.0000 NaN S \n", + "822 male 38.0 0 0 19972 0.0000 NaN S \n", + "732 male NaN 0 0 239855 0.0000 NaN S \n", + "674 male NaN 0 0 239856 0.0000 NaN S \n", + "\n", + "[891 rows x 12 columns]" + ] + }, + "execution_count": 19, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ - "# 请填写相应代码\n" + "titanic.sort_values(by='Fare', ascending=False)\n" ] }, { @@ -474,11 +1073,32 @@ }, { "cell_type": "code", - "execution_count": 14, + "execution_count": 20, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 20, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ - "# 请填写相应代码\n" + "titanic['Fare'].plot(kind='hist', bins=20, figsize=(10, 5))\n" ] }, { @@ -490,38 +1110,38 @@ }, { "cell_type": "code", - "execution_count": 15, + "execution_count": 21, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 21, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ - "# 请填写相应代码\n" + "titanic.plot(kind='scatter', x='Age', y='Fare', figsize=(10, 5))\n" ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] } ], "metadata": { "kernelspec": { - "display_name": "Python 3", + "display_name": "Python 3.10.7 ('.venv': venv)", "language": "python", "name": "python3" }, @@ -535,7 +1155,12 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.7.9" + "version": "3.10.7" + }, + "vscode": { + "interpreter": { + "hash": "1f0d395e06aa83586067b19165efc9b683889967164248deef4bbf1fa27cfb00" + } } }, "nbformat": 4,