q2

2023-04-09 20:58:59 +03:00
parent e3944d1833
commit e11dd8f72c
7 changed files with 221 additions and 52 deletions
--- a/YZM526/.idea/.gitignore
+++ b/YZM526/.idea/.gitignore
@@ -1,8 +0,0 @@
-# Default ignored files
-/shelf/
-/workspace.xml
-# Editor-based HTTP Client requests
-/httpRequests/
-# Datasource local storage ignored files
-/dataSources/
-/dataSources.local.xml
--- a/YZM526/.idea/YZM526.iml
+++ b/YZM526/.idea/YZM526.iml
@@ -2,7 +2,7 @@
 <module type="PYTHON_MODULE" version="4">
  <component name="NewModuleRootManager">
    <content url="file://$MODULE_DIR$" />
-    <orderEntry type="jdk" jdkName="Pipenv (YZM526)" jdkType="Python SDK" />
+    <orderEntry type="inheritedJdk" />
    <orderEntry type="sourceFolder" forTests="false" />
  </component>
 </module>
--- a/YZM526/.idea/workspace.xml
+++ b/YZM526/.idea/workspace.xml
@@ -0,0 +1,58 @@
+<?xml version="1.0" encoding="UTF-8"?>
+<project version="4">
+  <component name="AutoImportSettings">
+    <option name="autoReloadType" value="SELECTIVE" />
+  </component>
+  <component name="ChangeListManager">
+    <list default="true" id="fdff8b4f-ad89-4e24-b13f-eadf3aacfd67" name="Changes" comment="">
+      <change beforePath="$PROJECT_DIR$/.idea/.gitignore" beforeDir="false" />
+      <change beforePath="$PROJECT_DIR$/.idea/YZM526.iml" beforeDir="false" afterPath="$PROJECT_DIR$/.idea/YZM526.iml" afterDir="false" />
+      <change beforePath="$PROJECT_DIR$/Pipfile" beforeDir="false" afterPath="$PROJECT_DIR$/Pipfile" afterDir="false" />
+      <change beforePath="$PROJECT_DIR$/Pipfile.lock" beforeDir="false" afterPath="$PROJECT_DIR$/Pipfile.lock" afterDir="false" />
+      <change beforePath="$PROJECT_DIR$/vize.ipynb" beforeDir="false" afterPath="$PROJECT_DIR$/vize.ipynb" afterDir="false" />
+    </list>
+    <option name="SHOW_DIALOG" value="false" />
+    <option name="HIGHLIGHT_CONFLICTS" value="true" />
+    <option name="HIGHLIGHT_NON_ACTIVE_CHANGELIST" value="false" />
+    <option name="LAST_RESOLUTION" value="IGNORE" />
+  </component>
+  <component name="Git.Settings">
+    <option name="RECENT_GIT_ROOT_PATH" value="$PROJECT_DIR$/.." />
+  </component>
+  <component name="MarkdownSettingsMigration">
+    <option name="stateVersion" value="1" />
+  </component>
+  <component name="ProjectId" id="2O9iC2Dgpch3dAhc5Rv1H4TUFyk" />
+  <component name="ProjectViewState">
+    <option name="hideEmptyMiddlePackages" value="true" />
+    <option name="showLibraryContents" value="true" />
+  </component>
+  <component name="PropertiesComponent"><![CDATA[{
+  "keyToString": {
+    "RunOnceActivity.OpenProjectViewOnStart": "true",
+    "RunOnceActivity.ShowReadmeOnStart": "true",
+    "WebServerToolWindowFactoryState": "false",
+    "node.js.detected.package.eslint": "true",
+    "node.js.detected.package.tslint": "true",
+    "node.js.selected.package.eslint": "(autodetect)",
+    "node.js.selected.package.tslint": "(autodetect)",
+    "nodejs_package_manager_path": "npm",
+    "vue.rearranger.settings.migration": "true"
+  }
+}]]></component>
+  <component name="SpellCheckerSettings" RuntimeDictionaries="0" Folders="0" CustomDictionaries="0" DefaultDictionary="application-level" UseSingleDictionary="true" transferred="true" />
+  <component name="TaskManager">
+    <task active="true" id="Default" summary="Default task">
+      <changelist id="fdff8b4f-ad89-4e24-b13f-eadf3aacfd67" name="Changes" comment="" />
+      <created>1680979066279</created>
+      <option name="number" value="Default" />
+      <option name="presentableId" value="Default" />
+      <updated>1680979066279</updated>
+      <workItem from="1680979067356" duration="3117000" />
+    </task>
+    <servers />
+  </component>
+  <component name="TypeScriptGeneratedFilesManager">
+    <option name="version" value="3" />
+  </component>
+</project>
--- a/YZM526/Pipfile
+++ b/YZM526/Pipfile
@@ -15,5 +15,5 @@ stemgraphic = "*"
 [dev-packages]

 [requires]
-python_version = "3.10"
-python_full_version = "3.10.10"
+python_version = "3.11"
+python_full_version = "3.11.3"
--- a/YZM526/Pipfile.lock
+++ b/YZM526/Pipfile.lock
@@ -1,12 +1,12 @@
 {
    "_meta": {
        "hash": {
-            "sha256": "d8017b45fdf961da6c146ba442c8451f35daad3a765bb02ecab984b7a14e8c4f"
+            "sha256": "e8365f0fefdc8405e63dc85e51a910c63957618f9d7abb570a03435855318d82"
        },
        "pipfile-spec": 6,
        "requires": {
-            "python_full_version": "3.10.10",
-            "python_version": "3.10"
+            "python_full_version": "3.11.3",
+            "python_version": "3.11"
        },
        "sources": [
            {
@@ -25,6 +25,14 @@
            "markers": "python_full_version >= '3.6.2'",
            "version": "==3.6.2"
        },
+        "appnope": {
+            "hashes": [
+                "sha256:02bd91c4de869fbb1e1c50aafc4098827a7a54ab2f39d9dcba6c9547ed920e24",
+                "sha256:265a455292d0bd8a72453494fa24df5a11eb18373a60c7c0430889f22548605e"
+            ],
+            "markers": "platform_system == 'Darwin'",
+            "version": "==0.1.3"
+        },
        "argon2-cffi": {
            "hashes": [
                "sha256:8c976986f2c5c0e5000919e6de187906cfd81fb1c72bf9d88c01177e77da7f80",
@@ -92,11 +100,11 @@
        },
        "beautifulsoup4": {
            "hashes": [
-                "sha256:c7bdbfb20a0dbe09518b96a809d93351b2e2bcb8046c0809466fa6632a10c257",
-                "sha256:e44795bb4f156d94abb5fbc56efff871c1045bfef72e9efe77558db9f9616ac3"
+                "sha256:492bbc69dca35d12daac71c4db1bfff0c876c00ef4a2ffacce226d4638eb72da",
+                "sha256:bd2520ca0d9d7d12694a53d44ac482d181b4ec1888909b035a3dbf40d0f57d4a"
            ],
            "markers": "python_full_version >= '3.6.0'",
-            "version": "==4.12.1"
+            "version": "==4.12.2"
        },
        "bleach": {
            "hashes": [
--- a/YZM526/readme.md
+++ b/YZM526/readme.md
--- a/YZM526/vize.ipynb
+++ b/YZM526/vize.ipynb
@@ -3,7 +3,7 @@
  {
   "cell_type": "markdown",
   "source": [
-    "# SORU 3"
+    "# SORU 3 (1)"
   ],
   "metadata": {
    "collapsed": false
@@ -35,20 +35,29 @@
    "\n",
    "    print(title, space, \":\", indent, val)\n",
    "\n",
+    "\n",
    "def tprint(t):\n",
    "    dash = len(t) * \"-\"\n",
    "    print(t)\n",
    "    print(dash)"
   ],
   "metadata": {
-    "collapsed": false
+    "collapsed": false,
+    "ExecuteTime": {
+     "start_time": "2023-04-08T21:43:59.797089Z",
+     "end_time": "2023-04-08T21:43:59.827273Z"
+    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
-    "collapsed": true
+    "collapsed": true,
+    "ExecuteTime": {
+     "start_time": "2023-04-08T21:43:59.827188Z",
+     "end_time": "2023-04-08T21:43:59.854064Z"
+    }
   },
   "outputs": [
    {
@@ -83,7 +92,8 @@
    "# Soru 1-A, C, E\n",
    "import statistics\n",
    "\n",
-    "veri = [10, 14, 14, 17, 21, 21, 24, 27, 28, 30, 33, 35, 38, 41, 41, 42, 45, 51, 53, 54, 54, 55, 62, 71, 74, 76, 77, 77, 77, 88]\n",
+    "veri = [10, 14, 14, 17, 21, 21, 24, 27, 28, 30, 33, 35, 38, 41, 41, 42, 45, 51, 53, 54, 54, 55, 62, 71, 74, 76, 77, 77,\n",
+    "        77, 88]\n",
    "pprint(\"Verilerin Toplamı\", sum(veri))\n",
    "\n",
    "mean = statistics.mean(veri)\n",
@@ -139,10 +149,15 @@
    "# Soru 1-B\n",
    "# Dal - yaprak grafiği\n",
    "import stemgraphic\n",
+    "\n",
    "stemgraphic.stem_graphic(veri, asc=False)"
   ],
   "metadata": {
-    "collapsed": false
+    "collapsed": false,
+    "ExecuteTime": {
+     "start_time": "2023-04-08T21:43:59.854227Z",
+     "end_time": "2023-04-08T21:44:22.375718Z"
+    }
   }
  },
  {
@@ -162,6 +177,7 @@
    "# Soru 1-D\n",
    "# Çarpıklık\n",
    "from scipy.stats import skew\n",
+    "\n",
    "sk = skew(veri)\n",
    "if sk > 0:\n",
    "    yon = \"Sağ\"\n",
@@ -173,12 +189,16 @@
    "pprint(\"Çarpıklık Yönü\", yon)"
   ],
   "metadata": {
-    "collapsed": false
+    "collapsed": false,
+    "ExecuteTime": {
+     "start_time": "2023-04-08T21:44:22.344717Z",
+     "end_time": "2023-04-08T21:44:22.376411Z"
+    }
   }
  },
  {
   "cell_type": "code",
-   "execution_count": 21,
+   "execution_count": 35,
   "outputs": [
    {
     "name": "stdout",
@@ -191,17 +211,9 @@
      "[38, 52)          6\n",
      "[52, 66)          5\n",
      "[66, 80)          6\n",
-      "[80, 94)          1\n"
+      "[80, 94)          1\n",
+      "[10, 24, 38, 52, 66, 80, 94]\n"
     ]
-    },
-    {
-     "data": {
-      "text/plain": "          Frekans 0\n0                  \n[10, 24)          6\n[24, 38)          6\n[38, 52)          6\n[52, 66)          5\n[66, 80)          6\n[80, 94)          1",
-      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>Frekans 0</th>\n    </tr>\n    <tr>\n      <th>0</th>\n      <th></th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>[10, 24)</th>\n      <td>6</td>\n    </tr>\n    <tr>\n      <th>[24, 38)</th>\n      <td>6</td>\n    </tr>\n    <tr>\n      <th>[38, 52)</th>\n      <td>6</td>\n    </tr>\n    <tr>\n      <th>[52, 66)</th>\n      <td>5</td>\n    </tr>\n    <tr>\n      <th>[66, 80)</th>\n      <td>6</td>\n    </tr>\n    <tr>\n      <th>[80, 94)</th>\n      <td>1</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
-     },
-     "execution_count": 21,
-     "metadata": {},
-     "output_type": "execute_result"
    }
   ],
   "source": [
@@ -215,28 +227,24 @@
    "df = pd.DataFrame(veri)\n",
    "res = df.apply(lambda x: pd.cut(x, bins=binEdges, right=False).value_counts()).sort_index().add_prefix('Frekans ')\n",
    "print(res)\n",
-    "#res"
+    "print(binEdges)"
   ],
   "metadata": {
-    "collapsed": false
+    "collapsed": false,
+    "ExecuteTime": {
+     "start_time": "2023-04-08T22:23:21.965987Z",
+     "end_time": "2023-04-08T22:23:21.982373Z"
+    }
   }
  },
  {
   "cell_type": "code",
-   "execution_count": 15,
+   "execution_count": 39,
   "outputs": [
-    {
-     "data": {
-      "text/plain": "(array([6., 6., 6., 5., 6., 1.]),\n array([10., 24., 38., 52., 66., 80., 94.]),\n <BarContainer object of 6 artists>)"
-     },
-     "execution_count": 15,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
    {
     "data": {
      "text/plain": "<Figure size 640x480 with 1 Axes>",
-      "image/png": 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"
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"
     },
     "metadata": {},
     "output_type": "display_data"
@@ -246,10 +254,23 @@
    "# Soru 1-G / Histogram\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
-    "plt.hist(veri, bins=binEdges, edgecolor='black')"
+    "plt.hist(veri, bins=binEdges, edgecolor='black')\n",
+    "plt.axis([0, 100, 0, 7])\n",
+    "\n",
+    "plt.xticks([i for i in binEdges])\n",
+    "\n",
+    "plt.title(\"Soru 3(1) - G / Histogram\")\n",
+    "plt.xlabel(\"Gözlem Aralıkları\")\n",
+    "plt.ylabel(\"Frekans\")\n",
+    "\n",
+    "plt.show()\n"
   ],
   "metadata": {
-    "collapsed": false
+    "collapsed": false,
+    "ExecuteTime": {
+     "start_time": "2023-04-08T22:26:29.559883Z",
+     "end_time": "2023-04-08T22:26:29.620662Z"
+    }
   }
  },
  {
@@ -258,7 +279,7 @@
   "outputs": [
    {
     "data": {
-      "text/plain": "[<matplotlib.lines.Line2D at 0x7fd6f683d990>]"
+      "text/plain": "[<matplotlib.lines.Line2D at 0x136735fd0>]"
     },
     "execution_count": 7,
     "metadata": {},
@@ -279,9 +300,99 @@
    "kum = np.cumsum(values)\n",
    "plt.plot(base[1:], kum, marker=\"o\", linestyle='-')"
   ],
+   "metadata": {
+    "collapsed": false,
+    "ExecuteTime": {
+     "start_time": "2023-04-08T21:44:22.503169Z",
+     "end_time": "2023-04-08T21:44:22.599266Z"
+    }
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "# Soru 4 (2)"
+   ],
   "metadata": {
    "collapsed": false
   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 40,
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Cov                                       :    111.86666666666667\n",
+      "Cor                                       :      0.9987897067500333\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Soru 2-B\n",
+    "x = [5, 12, 20, 23, 30, 32]\n",
+    "y = [8, 16, 24, 28, 34, 36]\n",
+    "\n",
+    "# python 3.10'dan itibaren covarianca ve corelation dahili statistics modülüne eklendi\n",
+    "# <3.10 için numpy kullanılmalı ie:\n",
+    "# numpy.cov(x, y, bias=False)[0][1] // bis=False ise sample, yani n-1\n",
+    "import statistics\n",
+    "\n",
+    "cov = statistics.covariance(x, y)\n",
+    "pprint(\"Cov\", cov)\n",
+    "\n",
+    "# numpy.corrcoef(x,y)[0][1]\n",
+    "cor = statistics.correlation(x, y)\n",
+    "pprint(\"Cor\", cor)"
+   ],
+   "metadata": {
+    "collapsed": false,
+    "ExecuteTime": {
+     "start_time": "2023-04-08T22:26:49.765762Z",
+     "end_time": "2023-04-08T22:26:49.769079Z"
+    }
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [],
+   "metadata": {
+    "collapsed": false
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "outputs": [
+    {
+     "data": {
+      "text/plain": "<Figure size 640x480 with 1 Axes>",
+      "image/png": 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"
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Soru 2-C\n",
+    "import matplotlib.pyplot as plt\n",
+    "\n",
+    "plt.scatter(x, y)\n",
+    "plt.title(\"Soru 4(2) - C\")\n",
+    "plt.xlabel(\"x değerleri\")\n",
+    "plt.ylabel(\"y değerleri\")\n",
+    "plt.axis(\"equal\") # hem y hem de x ayn\n",
+    "plt.show()\n"
+   ],
+   "metadata": {
+    "collapsed": false,
+    "ExecuteTime": {
+     "start_time": "2023-04-08T22:16:27.237364Z",
+     "end_time": "2023-04-08T22:16:27.311169Z"
+    }
+   }
  }
 ],
 "metadata": {