diff --git a/Editor_Notebook.ipynb b/Editor_Notebook.ipynb
new file mode 100644
index 0000000000000000000000000000000000000000..8bd7904e5a9d903661e5de53d883304d7fb3f3d2
--- /dev/null
+++ b/Editor_Notebook.ipynb
@@ -0,0 +1,31 @@
+{
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "# Notebook for Coders"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": []
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": []
+ }
+ ],
+ "metadata": {
+ "language_info": {
+ "name": "python"
+ },
+ "orig_nbformat": 4
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/Notebook.ipynb b/Notebook.ipynb
index 975e65d6bc413385dd66bcd87057ce0c370f79b5..1960c5f05081b544405840850d4cf6693e0f636f 100644
--- a/Notebook.ipynb
+++ b/Notebook.ipynb
@@ -17,24 +17,18 @@
},
{
"cell_type": "code",
- "execution_count": 5,
+ "execution_count": null,
"metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "<src.Dataset.Dataset object at 0x000001CD53BDA250>\n"
- ]
- }
- ],
+ "outputs": [],
"source": [
"import matplotlib.pyplot as plt\n",
- "from src.Dataset import Dataset \n",
+ "from src.Dataset import Dataset\n",
+ "from src.Plotter import Plotter\n",
"\n",
"dataset = Dataset(\"data\\GamingStudy_data.csv\")\n",
"dataframe = dataset.get_dataframe()\n",
- "print(dataset)\n"
+ "print(dataset)\n",
+ "plotter = Plotter(dataset)"
]
},
{
@@ -58,20 +52,22 @@
"\n"
]
},
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Distribution of Participants \n",
+ "### Gender"
+ ]
+ },
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
- "\"\"\"\n",
- "4 Plots/Way to shows the distribution of \n",
- "Gender, \n",
- "Platform (where they found the survey)\n",
- "Games Top 5 \n",
- "and Console\n",
- "\n",
- "\"\"\""
+ "plotter.distribution_plot(\"Gender\")\n",
+ "pass"
]
},
{
@@ -266,7 +262,17 @@
"metadata": {},
"source": [
"## Q2 - Correlations between played hours and one's well being.\n",
- "**Maybe we can even add if hours watching Streams effect it**"
+ "**Maybe we can even add if hours watching Streams effect it**\n",
+ "\n",
+ "For research question two we wanted to know if there is a correlation \n",
+ "between played hours and the player's well being. We went into the question\n",
+ "with the expectation that players which play longer hours are more anxiety prone\n",
+ "and less satisfied with life than those who play less. If that would be the \n",
+ "case, a positive correlation of hours played and our combined anxiety score \n",
+ "variable would be expected. We want to take a look at the data using a scatter-\n",
+ "plot, showing the correlation of both variables of interest, using the\n",
+ "plot_scatterplot() function of our Plotter class:\n",
+ "code below: plotter.plot_scatterplot(\"Hours\", \"Anxiety_score\")"
]
},
{
@@ -275,10 +281,9 @@
"metadata": {},
"outputs": [],
"source": [
- " \"\"\"\n",
- " \n",
- " \n",
- " \"\"\""
+ "plotter.plot_scatterplot(\"Hours\", \"Anxiety_score\") \n",
+ "\n",
+ "#Still needs to be prettier"
]
},
{
@@ -304,37 +309,9 @@
},
{
"cell_type": "code",
- "execution_count": 13,
+ "execution_count": null,
"metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Category distribution:\n",
- "whyplay\n",
- "having fun 5105\n",
- "improving 4661\n",
- "winning 1977\n",
- "relaxing 623\n",
- "other 424\n",
- "all of the above 48\n",
- "dtype: int64\n"
- ]
- },
- {
- "data": {
- "image/png": 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bkqSGGNySJDXE4JYkqSEGtyRJDTG4JUlqiMEtSVJDDG5JkhpicEuS1BCDW5KkhhjckiQ1xOCWJKkhBrckSQ0xuCVJaojBLUlSQwxuSZIaYnBLktQQg1uSpIYY3JIkNcTgliSpIQa3JEkNMbglSWqIwS1JUkMMbkmSGmJwS5LUEINbkqSGGNySJDXE4JYkqSEGtyRJDTG4JUlqiMEtSVJDDG5JkhqyYK4roDY96sjTuenW2wdW/uIjTh1IuTtsu5CL3v6MgZQtScNgcGtGbrr1dtYcdeBAyh4bG2N0dHQgZQ/qC4EkDYuHyiVJaojBLUlSQwxuSZIaYnBLktQQg1uSpIZMGdxJ9kiyIsklSS5O8to6fHmSK5NcWF8HDL66kiRt2abzc7A7gNeXUr6bZHvggiRfq+PeX0p53+CqJ0mSek0Z3KWUq4CravfNSVYDuw+6YpIkaX0bdQOWJIuBRwPnAU8E/irJIcBKur3yGyaYZxmwDGBkZISxsbFNrPLE/vKMW7hlcDfyGtiNO7ZbCB966nYDKXvQBvVerl27dmBlw+DqPUhu3/NPi9thy+ZVe5dSpvUCFgEXAM+v/SPA1nTnyd8FHDNVGfvuu28ZlAe96ZSBlb1ixYqBlT3Ieg+S7T1ctvf8YrsMV6vtDawsE2TptK4qT7IQ+AJwfCnlizXwryml3FlKuQv4KPDYWf5OIUmS+kznqvIAHwdWl1L+qWf4rj2TPQ9YNfvVkyRJvaZzjvuJwEuAHyS5sA77O+BFSfYBCrAGeMUA6idJknpM56ryc4BMMOors18dSZK0Id45TZKkhhjckiQ1xOCWJKkhBrckSQ3ZqDunSdKW4FFHns5Ntw7wVnUM7m51O2y7kIve/oyBlD1Ig27z+dTeBrck9bnp1ttZc9SBAyt/bGyM0dHRgZQ9qIAatEG2+Xxrbw+VS5LUEINbkqSGGNySJDXE4JYkqSEGtyRJDTG4JUlqiMEtSVJDDG5JkhpicEuS1BCDW5KkhhjckiQ1xOCWJKkhBrckSQ0xuCVJaojBLUlSQwxuSZIaYnBLktQQg1uSpIYY3JIkNcTgliSpIQa3JEkNMbglSWqIwS1JUkMMbkmSGmJwS5LUEINbkqSGGNySJDVkwVxXQG3a/hFH8MjjjhjcAo4bTLHbPwLgwMEULklDYHBrRm5efRRrjhpMAI6NjTE6OjqQshcfcepAypWkYfFQuSRJDTG4JUlqiMEtSVJDDG5JkhpicEuS1JApgzvJHklWJLkkycVJXluH75Tka0l+XP/eb/DVlSRpyzadPe47gNeXUvYCHg/8ZZK9gCOAM0opDwPOqP2SJGmApgzuUspVpZTv1u6bgdXA7sBzWXebjOOAgwZUR0mSVG3UDViSLAYeDZwHjJRSrqqjrgZGJplnGbAMYGRkhLGxsZnWdYNavpPX2Nh2gyl8wAb1Xq5du3ZgZcPg6j1Ibt/DN8jtxG18Yn6mTFMpZVovYBFwAfD82n9j3/gbpipj3333LYPyoDedMrCyV6xYMbCyB1nvQbK9h8v2Hq5B19s2X5/b+PqAlWWCLJ3WVeVJFgJfAI4vpXyxDr4mya51/K7AtbP6jUKSJK1nOleVB/g4sLqU8k89o04GDq3dhwInzX71JElSr+mc434i8BLgB0kurMP+DjgKOCHJ4cDlwJ8NpIaSJOluUwZ3KeUcIJOMfursVkeSJG2Id06TJKkhBrckSQ0xuCVJaojBLUlSQzbqzmmStCUY+J3qYKB3q4MDB1O4NgsGtyT1uXn1Uaw5anDhNzY2xujo6EDKXnzEqQMpV5sPD5VLktQQg1uSpIYY3JIkNcTgliSpIQa3JEkNMbglSWqIwS1JUkP8Hbckac4N/KY38+iGNwa3JGnODfKmN/PthjceKpckqSEGtyRJDTG4JUlqiMEtSVJDDG5JkhpicEuS1BCDW5KkhhjckiQ1xOCWJKkhBrckSQ0xuCVJaojBLUlSQwxuSZIaYnBLktQQg1uSpIYY3JIkNcTgliSpIQa3JEkNMbglSWqIwS1JUkMMbkmSGrJgriswmxYfcergCj9tMGXvsO3CgZQrSZqf5k1wrznqwIGVvfiIUwdaviRJ0+WhckmSGmJwS5LUkCmDO8kxSa5Nsqpn2PIkVya5sL4OGGw1JUkSTG+P+1jgmRMMf38pZZ/6+srsVkuSJE1kyuAupZwFXD+EukiSpClsylXlf5XkEGAl8PpSyg0TTZRkGbAMYGRkhLGxsU1Y5Nxptd6D1OLP77Zb2O57Oah6r127dqBtYnuvzzafmNv4NJVSpnwBi4FVPf0jwNZ0e+zvAo6ZTjn77rtvadGD3nTKXFdhi2J7r2+QbbJixYqBld3qeznoetvm63MbXx+wskyQpTO6qryUck0p5c5Syl3AR4HHzsJ3CEmSNIUZBXeSXXt6nwesmmxaSZI0e6Y8x53k08AosHOSK4C3A6NJ9gEKsAZ4xeCqKEmSxk0Z3KWUF00w+OMDqIskSZqCd06TJKkhBrckSQ0xuCVJaojBLUlSQ+bN87il+a7FO9XtsO3CgZQ7DANtb7DNNWMGt9SANUcdOLCyFx9x6kDLb9Gg28M216bwULkkSQ0xuCVJaojBLUlSQwxuSZIaYnBLktQQg1uSpIYY3JIkNcTgliSpId6ARZK0WfDugNNjcEuS5px3B5w+D5VLktQQg1uSpIYY3JIkNcTgliSpIQa3JEkNMbglSWqIwS1JUkMMbkmSGmJwS5LUEINbkqSGGNySJDXE4JYkqSEGtyRJDTG4JUlqiMEtSVJDDG5JkhpicEuS1BCDW5KkhhjckiQ1xOCWJKkhBrckSQ0xuCVJaojBLUlSQwxuSZIaMmVwJzkmybVJVvUM2ynJ15L8uP6932CrKUmSYHp73McCz+wbdgRwRinlYcAZtV+SJA3YlMFdSjkLuL5v8HOB42r3ccBBs1stSZI0kQUznG+klHJV7b4aGJlswiTLgGUAIyMjjI2NzXCRc6vVerfK9h4u23v4bPPhmk/tPdPgvlsppSQpGxh/NHA0wJIlS8ro6OimLnL4TjuVJuvdKtt7uGzv4bPNh2uetfdMryq/JsmuAPXvtbNXJUmSNJmZBvfJwKG1+1DgpNmpjiRJ2pDp/Bzs08C3gd9PckWSw4GjgKcn+THwtNovSZIGbMpz3KWUF00y6qmzXBdJkjQF75wmSVJDDG5JkhpicEuS1BCDW5KkhhjckiQ1xOCWJKkhBrckSQ0xuCVJaojBLUlSQwxuSZIaYnBLktQQg1uSpIYY3JIkNcTgliSpIVM+1nO+SrJx079n48ovpWzcDJIkTcMWu8ddSpn2a8WKFRs1vaEtSRqULTa4JUlqkcEtSVJDDG5JkhpicEuS1BCDW5KkhhjckiQ1xOCWJKkhBrckSQ0xuCVJaojBLUlSQwxuSZIaYnBLktQQg1uSpIYY3JIkNcTgliSpIQa3JEkNMbglSWqIwS1JUkMMbkmSGmJwS5LUEINbkqSGGNySJDXE4JYkqSEGtyRJDVmwKTMnWQPcDNwJ3FFKWTIblZIkSRPbpOCulpZSrpuFciRJ0hQ8VC5JUkNSSpn5zMllwA1AAT5SSjl6gmmWAcsARkZG9v3MZz4z4+XNlbVr17Jo0aK5rkbTli5dOtDyV6xYMdDyW2N7D5ftPVxbSnsvXbr0golOQW9qcO9eSrkyyQOArwGvLqWcNdn0S5YsKStXrpzx8ubK2NgYo6Ojc12NLYbtPVy29/DZ5sPVansnmTC4N+lQeSnlyvr3WuBE4LGbUp4kSdqwGQd3ku2SbD/eDTwDWDVbFZMkSevblKvKR4ATk4yX86lSymmzUitJkjShGQd3KeVS4FGzWBdJkjQFfw4mSVJDDG5JkhpicEuS1BCDW5KkhhjckiQ1xOCWJKkhBrckSQ0xuCVJaojBLUlSQzbp6WAbvbDkF8DlQ1vg7NkZuG6uK7EFsb2Hy/YePtt8uFpt7weVUnbpHzjU4G5VkpUTPVpNg2F7D5ftPXy2+XDNt/b2ULkkSQ0xuCVJaojBPT1Hz3UFtjC293DZ3sNnmw/XvGpvz3FLktQQ97glSWqIwS1JUkOaCO4ka5LsXLvXbuS8r0myOsnxfcP3SXJAT//yJG+YnRrfYzljSZr/GUKSxUlWzVJZ70jytFkq60/r+7tiNsrbHCX51lzXASDJK5McMtf12Jxt7OdTz3y7Jfn8bNdnvkrylSQ7znDeJUn+dZarNFQL5roCQ/Aq4GmllCv6hu8DLAG+MvQabeFKKW+bxeIOB15eSjlnFsvcrJRS/sdsl5lkQSnljo2sx4dnux4tShK664Pumq0ySyk/B14wW+XNd6WUA6aeatJ5VwIrZ7E6Q7dZ7XEn+VKSC5JcnGTZRs77uiSr6uuv67APAw8Gvprkb3qmvRfwDuCFSS5M8sI6aq+6h3xpktf0TP/iJOfXaT+SZOsJlv+2JN+pyz+6/nOPe0mdd1WSx9bpd6rr+/0k5yb5wyRb1aMLO/aU++MkI0l2SfKFuozvJHnixrTPLNk6yUfr+3N6km1rHV9e63RRreN9kuyQ5PIkW9VptkvysyQLkxyb5AV1+JokRyb5bpIfJHl4Hb5Lkq/VZX2slrVzb2WSvA3YD/h4kvcmOSzJB3vGn5JktHavTfKuWsdzk4wMo8Fmw/heXJLRJGcmOaluo0clObhumz9I8pA63bFJPpxkZZIfJXl2HX5YkpOTfAM4Ywbb4N1Hper/yXvqsn+UZP86/D5JTkhySZITk5yX+XPE6b+SfAJYBby1bvPfT3LkBNMvSnJGz3b93Dr8MXWeber/xMVJ9k7PEa36Pn0xyWm17f+xp9zDa3ufX/8XP9i/7PkgyRtTP4OTvL9usyR5SpLj6za6c2231ZN8Lk22jY4mOaV2L09yTCb+3H9rfc/PSfLpDOCI7IyVUjabF7BT/bst3T/H/Wv/GmDn2r12gvn2BX4AbAcsAi4GHt0/b988hwEf7OlfDnwLuDfd7fF+CSwEHgF8GVhYp/s34JDJ6l67/wN4Tu0eAz5au58ErKrdHwDeXrufAlxYu/8FeGntfhzw9dr9KWC/2r0nsHrI781i4A5gn9p/AvDi2n3/nuneCby6dp8ELK3dLwQ+VruPBV7Q8/6MT/+qnmk+CLy5dj8TKJO8j2PAkkne01OA0dpdet6TfwTeMtfb+0a0/dr6dxS4Edi1bqdXAkfWca8F/rmnfU+j+2L+MOAKYJvaPlew7v9sY7fB5cAbetr9/9buA3qmeQPwkdq9d91mlsx1G87S9n8X8HjgGXQ/L0pt41OAJ/W9VwuA+9bunYGfsO5XPO8E3gd8qGcbX8y6z4bDgEuBHer7djmwB7Bb/X/Zie6z6eze7X0+vWo7f652nw2cX9f57cArajvszIY/lybbRkeBU3q26Yk+9x8DXFjbf3vgx+Pb/ubw2qz2uIHXJLkIOJduQ33YNOfbDzixlHJLKWUt8EVg/xks/9RSym2llOuAa4ER4Kl0Xwy+k+TC2v/gCeZdWvcufkD3IfgHPeM+DVBKOQu4b92b2Y8u4CmlfAO4f5L7Ap+lCzmAP6/9AE8DPljrcHItZ9EM1nFTXFZKubB2X0D3TwOwd5Kz67ofzLp1n2xd+n1xgjL3Az4DUEo5DbhhE+v+W7oP2P7ltOY7pZSrSim3Af8NnF6H/4B7rtMJpZS7Sik/pguBh9fhXyulXF+7N3Yb7DfV+7YK+P4M1nFzdXkp5Vy64H4G8D3gu3Rt2/9ZFeDdSb4PfB3Yne7zBLqjfU+nO1X3j0zsjFLKTaWU3wCXAA8CHgucWUq5vpRyO/C5WVuzzc8FwL51e7wN+DZde+1PF+S9Jvtcgom30X4Tfe4/ETiplPKbUsrNdDtvm43N5hx3PaT5NOAJpZRfJxmj+7YzTLf1dN9J1z4BjiulvHmymZJsQ7cnvqSU8rMky7ln3ft/LL+hH89/G3hokl2Ag+i+nUP3zf7x9R95rvS3z7a1+1jgoFLKRUkOo/tGC90XjHcn2Ynuy883pih3vM1n6g7uefqn9z24vdSv2LOwnLnU+x7c1dN/F/dcp8m2uVumsYzJtsHJ6tJye26M8bYL8A+llI9sYNqDgV2AfUsptydZw7rt8f50RwYX1mETvScTfRZtMWqbXUZ39OFbdF8AlwIPBVb3TT7Z51LvuA21YXNtvTntce8A3FBD++F0h0qm62zgoHp+bTvgeaz/razfzXSHQKZyBvCCJA+Au89NP6hvmvF/yOvqXnD/RSYvrPPuB9xUSrmp1u/gOnwUuK6U8qsaLicC/0R3OPyXtYzTgVePF5hkn2nUfVi2B65KspC6TgD16Md36A69nlJKuXMjyvwm8GcASZ4B3G8a86wB9qnnafeg20PZUv1pbYeH0B0h+q8JptnYbXA6et+3vYBHzngNNl//Cbxs/IhXkt3HPx967ABcWwNoKd0e87iPAG8FjgfesxHL/Q7w5CT3S7IA+JMZr0EbzqY79XJW7X4l8L2eL+CD9E3gOfVahEXAs4ewzGnbnL5ZnAa8Mslqug+Zc6c7Yynlu0mOpTsPAt150u9NMdsK4Ih66PkfNlD2JUneApye7kKr24G/pOfxpKWUG5N8lO68/NV0/2C9fpPke3TfsF9Why0HjqmH0n4NHNoz/WdrGYf1DHsN8KE6/QK6jfmVU6zjsLwVOA/4Rf3b+4Xos3SH9EY3sswjgU8neQndHuDVdF+2NuSbwGV0hxZX0x3G3FL9lO7/4b7AK0spv8k9rpcENn4bnI5/A45LcgnwQ7rrTW7a2Mpvzkoppyd5BPDt2qZrgRfTHWYddzzw5Xr6aCVdW5Du53S3l1I+le4i128leQrd6YyplntlknfTva/X1zLnVdv2ORv4P8C3Sym3JPkNU++QzYpSyneSnEy3p38N3amozaatveWpNktJ7g3cWUq5I8kTgP9XStlnjqvVhPol9pRSytB/F1zDaGH9ovAQuvO7v19K+e2w6zIfJVlUSllb97hPBI4ppZw41/Waj3ra+j50O0rLSimbxc7A5rTHLfXaEzihHuX4LfDyOa6Ppuc+wIp62iTAqwztWbU83c2LtqE7ffalua3OvHZ0Pd2zDd11TptFaIN73JIkNWVzujhNkiRNweCWJKkhBrckSQ0xuKV5JjN8QtUE5dzj3u+SNg8GtyRJDTG4pcZM9eSk2n2PJ6El2T7JZfVnWiS573h/fTLSv6TvCXZ9y3xOvRf/95J8vZa5VbqnV+1Sp9kqyU/G+yUNhsEtteds1j1EZwmwqAby/nQ3itgOOLeU8qja//L6oIQx4MA6358DX6wPqwC4T73BzauAYyZY5jl098p/NN1DRP62dM+j/iTrbnP7NOCiUsovZmtFJa3P4JbaM9WTkyZ7EtrHgJfW7pcC/95T5kRPsOv1QOA/6y0838i6J8AdAxxSu1/WV6akATC4pcbUveTeJyedzT2fnDThk9BKKd8EFtcHimxdH7t5d7H9i+nr/wDds58fSfc85G1qmT8Drqn3234s8NVNX0NJG2JwS22a6ZOTPgF8ivX3jCd6gl2vHYAra/ehfeM+RnfI/HMb+QQ4STNgcEttOhvYle7JSdcA031y0vF0j0j9dN/w8SfYfRg4fIL5lgOfS3IBcF3fuJPpni/tYXJpCLxXubQFSfIC4LmllJf0DBsD3lBKWTnDMpcA7y+l7D/lxJI2mU8Hk7YQST4APAs4YBbLPAL4C9ZdWS5pwNzjliSpIZ7jliSpIQa3JEkNMbglSWqIwS1JUkMMbkmSGvL/AWc2sNYw3beUAAAAAElFTkSuQmCC",
- "text/plain": [
- "<Figure size 576x432 with 1 Axes>"
- ]
- },
- "metadata": {
- "needs_background": "light"
- },
- "output_type": "display_data"
- }
- ],
+ "outputs": [],
"source": [
"\"\"\" Horizontal bar chart, one row for every reason for with top width\n",
"# Anxiety colored in for the amount of anxiety in that group \n",
@@ -444,4 +421,4 @@
},
"nbformat": 4,
"nbformat_minor": 2
-}
\ No newline at end of file
+}
diff --git a/src/Plotter.py b/src/Plotter.py
index c8d9051a3b7e2319a463cf91aba90b003908e7a9..5b0c4271991ac2a2a66867b6fc69f6e2f8cb59dd 100644
--- a/src/Plotter.py
+++ b/src/Plotter.py
@@ -15,7 +15,7 @@ class Plotter:
self.df = dataset.get_dataframe()
def customize_plot(self, fig, ax, styling_params) -> None:
- """ customize_plot
+ """customize_plot
Args:
fig (plt.figure.Figure),
@@ -72,7 +72,7 @@ class Plotter:
def plot_categorical_bar_chart(
self, category1, category2, styling_params={}
) -> None:
- """ plot a categorical bar chart.
+ """plot a categorical bar chart.
Args:
category1 (str, must be present as a column in the dataset),
@@ -119,7 +119,7 @@ class Plotter:
def plot_categorical_boxplot(
self, target, category, styling_params={}
) -> None:
- """ plot a categorical boxplot.
+ """plot a categorical boxplot.
Args:
target (str, must be present as a column in the dataset),
@@ -130,32 +130,7 @@ class Plotter:
Returns:
None
"""
- # implementing sensible logging and error catching
- if (type(target) != str):
- logging.error("parameter target should be a string.")
- raise ValueError("parameter target should be a string.")
-
- if not (target in self.df.columns):
- logging.error("parameter target cannot be found in the dataset.")
- raise ValueError(
- "parameter target cannot be found in the dataset."
- )
-
- if (type(category) != str):
- logging.error("parameter category should be a string.")
- raise ValueError("parameter category should be a string.")
-
- if not (category in self.df.columns):
- logging.error("parameter category cannot be found in the dataset.")
- raise ValueError(
- "parameter category cannot be found in the dataset."
- )
-
- if (type(styling_params) != dict):
- logging.error("parameter styling params should be a dict.")
- raise ValueError("parameter styling params should be a dict.")
- # plotting the plot
fig, ax = plt.subplots()
self.customize_plot(fig, ax, styling_params)
sns.boxplot(x=category, y=target, data=self.df, palette="rainbow")
@@ -163,7 +138,7 @@ class Plotter:
def plot_categorical_histplot(
self, target, category, styling_params={}, bins=30
) -> None:
- """ plot a categorical hisplot.
+ """plot a categorical hisplot.
Args:
target (str, must be present as a column in the dataset),
@@ -215,7 +190,7 @@ class Plotter:
)
def plot_scatterplot(self, target1, target2, styling_params={}) -> None:
- """ plot a scatterplot.
+ """plot a scatterplot.
Args:
target1 (str, must be present as a column in the dataset),
@@ -255,4 +230,23 @@ class Plotter:
# plotting the plot
fig, ax = plt.subplots()
self.customize_plot(fig, ax, styling_params)
- ax.scatter(self.df[target1], self.df[target2])
\ No newline at end of file
+ ax.scatter(self.df[target1], self.df[target2])
+
+ def distribution_plot(self, target: str):
+ """
+ distribution_plot _summary_
+
+ Args:
+ target (str): _description_
+
+ Returns:
+ None
+ """
+ grouped_data = self.df.groupby(target).size()
+ plt.barh(grouped_data.index, grouped_data.values)
+ print(grouped_data.sort_values(ascending=False))
+ # print(grouped_data.index)
+ # print(grouped_data.values)
+ plt.xlabel("Size")
+ plt.ylabel(target)
+ plt.title(f"Distribution of {target}")