From cbbf294dc30854463ff0fa0c158359c824d9006e Mon Sep 17 00:00:00 2001
From: zoonalink <zoonalink@gmail.com>
Date: Sat, 24 Dec 2022 15:23:32 +0000
Subject: [PATCH] FR10+ working on plots in order

---
 T2-FR10.ipynb   | 326 ++++++++++++++++++++++++++++++++++--------------
 Task2-ALL.ipynb |  49 +++++++-
 2 files changed, 281 insertions(+), 94 deletions(-)

diff --git a/T2-FR10.ipynb b/T2-FR10.ipynb
index 05e373f..aac9dbb 100644
--- a/T2-FR10.ipynb
+++ b/T2-FR10.ipynb
@@ -2,7 +2,7 @@
  "cells": [
   {
    "cell_type": "code",
-   "execution_count": 115,
+   "execution_count": 1,
    "metadata": {},
    "outputs": [
     {
@@ -21,7 +21,7 @@
        "<IPython.core.display.HTML object>"
       ]
      },
-     "execution_count": 115,
+     "execution_count": 1,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -41,7 +41,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 304,
+   "execution_count": 2,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -61,7 +61,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 415,
+   "execution_count": 3,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -82,9 +82,12 @@
    "source": [
     "## <font color = 'orange'> Further Data Cleansing </font>\n",
     "\n",
-    "The data has been 'cleaned' in FR8 and FR9 - by removing the rows with missing values and dropping columns, as per specification. \n",
+    "The data has been 'cleaned' in FR8 and FR9:\n",
     "\n",
-    "In this notebook, we will further clean the data by removing outliers and duplicates.\n",
+    "* removing rows with missing values\n",
+    "* dropping columns ('region', 'final_result', 'highest_education'), as per specification. \n",
+    "\n",
+    "I will further clean the data by removing duplicates and considering outliers.\n",
     "\n",
     "### <font color = 'gold'> Duplicates </font>\n",
     "\n",
@@ -94,13 +97,19 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 416,
+   "execution_count": 4,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
+      "\n",
+      "Number of unique / duplicate rows:\n",
+      "\n",
+      "False    22134\n",
+      "True      3125\n",
+      "dtype: int64\n",
       "\n",
       "Examples of duplicate rows:\n",
       "\n",
@@ -114,29 +123,24 @@
       "715         27891      M     0-35          Y   70.0         348.0\n",
       "716         27891      M     0-35          Y   70.0         348.0\n",
       "9742        29639      M     0-35          N   84.0       10503.0\n",
-      "9741        29639      M     0-35          N   84.0       10503.0\n",
-      "\n",
-      "Number of unique / duplicate rows:\n",
-      "False    22134\n",
-      "True      3125\n",
-      "dtype: int64\n"
+      "9741        29639      M     0-35          N   84.0       10503.0\n"
      ]
     }
    ],
    "source": [
     "# isolate duplicate rows subsetting on student id (assumed to be unique)\n",
     "duplicates = students1[students1.duplicated(subset=['id_student'], keep=False)]\n",
-    "# visually inspect duplicate rows\n",
-    "print(\"\\nExamples of duplicate rows:\\n\")\n",
-    "print(duplicates.sort_values(by=['id_student']).head(10))\n",
     "\n",
-    "print(\"\\nNumber of unique / duplicate rows:\")\n",
-    "print(students1.duplicated().value_counts())\n"
+    "print(\"\\nNumber of unique / duplicate rows:\\n\")\n",
+    "print(students1.duplicated().value_counts())\n",
+    "\n",
+    "print(\"\\nExamples of duplicate rows:\\n\")\n",
+    "print(duplicates.sort_values(by=['id_student']).head(10))"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 417,
+   "execution_count": 5,
    "metadata": {},
    "outputs": [
     {
@@ -166,6 +170,7 @@
    "source": [
     "# remove duplicate rows\n",
     "students = students1.drop_duplicates()\n",
+    "\n",
     "print(\"\\nDataframe summary after removing duplicates:\\n\")\n",
     "print(students.info())"
    ]
@@ -177,14 +182,14 @@
    "source": [
     "### <font color = 'gold'> Outliers </font>\n",
     "\n",
-    "In the course of the exploratory data analysis (EDA), I decided to remove extreme outliers, as follows: \n",
+    "Following exploratory data analysis (EDA), I made these <i>conservative</i> decisions regarding outliers: \n",
     "\n",
-    "1. Students with <u>`clicks greater than 25000`</u> were removed with the rationale that these students are not typical of the population.  There were a total of 3 students - shown below."
+    "1. Students with `'click_events' greater than 25000` were removed with the rationale that these students are not typical of the population.  There were a total of 3 students - shown below:"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 418,
+   "execution_count": 6,
    "metadata": {},
    "outputs": [
     {
@@ -201,6 +206,7 @@
    "source": [
     "# students with high click events\n",
     "stu_highclick = students.loc[(students['click_events']>=25000)]\n",
+    "\n",
     "#print(stu_highclick.shape)\n",
     "print(stu_highclick)"
    ]
@@ -210,12 +216,12 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "2. Students with <u>`scores of 0 and fewer than 500 clicks`</u> were also removed, with the rationale that these students most likely never properly engaged with the course, and as such leaving them will obfuscate the analysis.  There were a total of 22 such students - shown below."
+    "2. Students with `scores of 0 and fewer than 500 clicks` were also removed with the rationale that these students most likely never properly engaged with the course, and as such leaving them will obfuscate the analysis.  There were a total of 22 such students - shown below."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 419,
+   "execution_count": 7,
    "metadata": {},
    "outputs": [
     {
@@ -249,14 +255,16 @@
     }
    ],
    "source": [
+    "# students with low click events and score of 0\n",
     "stu_lowScore_lowClick = students.loc[(students['score']==0) & (students['click_events']<=500)]\n",
+    "\n",
     "#print(stu_lowScore_lowClick.shape)\n",
     "print(stu_lowScore_lowClick)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 420,
+   "execution_count": 8,
    "metadata": {},
    "outputs": [
     {
@@ -281,10 +289,11 @@
     }
    ],
    "source": [
-    "# remove outliers\n",
+    "# remove outliers identified above\n",
     "students = students.loc[~((students['score']==0) & (students['click_events']<=500))]\n",
     "students = students.loc[~(students['click_events']>=25000)]\n",
     "\n",
+    "\n",
     "print(students.info())"
    ]
   },
@@ -295,26 +304,25 @@
    "source": [
     "## <font color = 'orange'> Data Preparation for EDA </font>\n",
     "\n",
-    "During iterative EDA, I created a number of new dataframes / variables, which I have collated into the cell below.  These will be explained further below as part of the EDA process.\n",
-    "\n"
+    "During iterative EDA, I created a number of new dataframes / variables, which I have collated into the cell below.  These will be explained further as they feature in the EDA process."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 424,
+   "execution_count": 9,
    "metadata": {},
    "outputs": [],
    "source": [
     "#create new column of the log of click events - explained in report where clicks are transformed\n",
     "students['log_clicks'] = np.log10(students['click_events'])\n",
     "\n",
-    "\n",
-    "#disability recoded to 1/0 from Y/N\n",
+    "#new categorical disability recoded to 1/0 from Y/N\n",
     "students['disability_cat'] = pd.Categorical(students['disability'].replace({'Y': 1, 'N': 0}))\n",
+    "students['id_student'] = students['id_student'].astype('category')\n",
     "\n",
-    "\n",
-    "# isolating columns for use in analysis\n",
+    "# isolating columns for use in distribution, scatter plots\n",
     "clicks = students['click_events']\n",
+    "log_clicks = np.log10(students['click_events'])\n",
     "scores = students['score']\n",
     "click_scores = students[['click_events', 'score']]\n",
     "\n",
@@ -326,15 +334,23 @@
     "disabled_yes = students[students['disability']=='Y']\n",
     "disabled_no = students[students['disability']=='N']\n",
     "\n",
-    "#students.age_band.value_counts().plot(kind='bar')\n",
-    "\n",
     "# isolating by age band for use in subsetting\n",
     "age_0_35 = students[students['age_band']=='0-35']\n",
     "age_35_55 = students[students['age_band']=='35-55']\n",
     "age_55_ = students[students['age_band']=='55<=']\n",
+    "\n"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## <font color = 'orange'> FR10\tInvestigate the effects of engagement on attainment </font>\n",
     "\n",
-    "# log of click events\n",
-    "log_clicks = np.log10(students['click_events'])"
+    "Now that the data has been cleaned and prepared, I will investigate the effects of engagement on attainment.  \n",
+    "\n",
+    "I will start by considering the whole dataset before looking at distributions of the individual variables and then considering whether relationships. "
    ]
   },
   {
@@ -342,46 +358,84 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "## FR10\tInvestigate the effects of engagement on attainment\n",
+    "### <font color = 'gold'> Students dataset </font>\n",
+    "\n",
+    "Looking at the summary statistics of the numerical values below, we can see that scores range from 0 to 100 with a mean of 74 and a standard deviation of 15.  Clicks range from 10 to 25000 (as per data preparation) with a mean of 1700 and a standard deviation of 1975.  The large standard deviation for clicks suggests that there is a large spread of values and that it is right-skewed.\n",
     "\n",
-    "Now that the data has been cleaned and prepared, I will investigate the effects of engagement on attainment.  I will do this by looking at the relationship between the 'click_events' and the 'score.' \n",
+    "The bar charts below show that there are slightly more male students than female students and that there are many more younger students (18-35) with a very few aged over 55.  Approximately 10% of students have declared a disability.\n",
     "\n",
-    "I will start by considering the whole dataset before looking at individual distrubutions of the individual variables and then considering whether there is a relationship between these continuous variables. "
+    "I will return to these summary statistics later in the notebook but first, let's look at the distributions of the variables."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 427,
+   "execution_count": 10,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "         id_student         score  click_events    log_clicks\n",
-      "count  2.210900e+04  22109.000000  22109.000000  22109.000000\n",
-      "mean   7.098895e+05     73.702926   1699.504817      2.971034\n",
-      "std    5.569954e+05     15.294032   1974.542987      0.515953\n",
-      "min    6.516000e+03      0.000000     10.000000      1.000000\n",
-      "25%    5.045890e+05     66.000000    434.000000      2.637490\n",
-      "50%    5.895480e+05     77.000000   1009.000000      3.003891\n",
-      "75%    6.450030e+05     85.000000   2249.000000      3.351989\n",
-      "max    2.698588e+06    100.000000  24368.000000      4.386820\n"
+      "              score  click_events    log_clicks\n",
+      "count  22109.000000  22109.000000  22109.000000\n",
+      "mean      73.702926   1699.504817      2.971034\n",
+      "std       15.294032   1974.542987      0.515953\n",
+      "min        0.000000     10.000000      1.000000\n",
+      "25%       66.000000    434.000000      2.637490\n",
+      "50%       77.000000   1009.000000      3.003891\n",
+      "75%       85.000000   2249.000000      3.351989\n",
+      "max      100.000000  24368.000000      4.386820\n"
      ]
     }
    ],
    "source": [
-    "print(students.describe())\n"
+    "print(students.describe())"
    ]
   },
   {
-   "attachments": {},
-   "cell_type": "markdown",
+   "cell_type": "code",
+   "execution_count": 11,
    "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1000x500 with 3 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
    "source": [
-    "Looking at the summary statistics, we can see that the mean score is 74 with a standard deviation of 15; and the mean clicks is 1700 with a standard deviation of 1975.  The large standard deviation for clicks suggests that there is a large spread of values, ranging from 10 to 25000 (as per previous data preparation).\n",
-    "\n",
-    "We will return to these summary statistics later in the notebook but first, let's look at the distribution of the variables."
+    "# set figure \n",
+    "fig, axes = plt.subplots(1, 3, figsize=(10, 5), sharey=True)\n",
+    "fig.suptitle('Counts of students')\n",
+    "\n",
+    "# first subplot (matplotlib bar chart)\n",
+    "plt.subplot(1,3,1)\n",
+    "students.age_band.value_counts().plot(kind='bar',)\n",
+    "plt.xlabel('Age band')\n",
+    "plt.ylabel('Count')\n",
+    "plt.xticks(rotation=0)\n",
+    "\n",
+    "# second subplot (matplotlib bar chart)\n",
+    "plt.subplot(1,3,2)\n",
+    "students.gender.value_counts().plot(kind='bar')\n",
+    "plt.xticks(rotation=0)\n",
+    "plt.xlabel('Gender')\n",
+    "\n",
+    "# third subplot (matplotlib bar chart)\n",
+    "plt.subplot(1,3,3)\n",
+    "students.disability.value_counts().plot(kind='bar')\n",
+    "plt.xlabel('Disability')\n",
+    "plt.xticks(rotation=0)\n",
+    "\n",
+    "# prevent overlapping of subplots\n",
+    "plt.tight_layout()\n",
+    "plt.show()\n",
+    "\n"
    ]
   },
   {
@@ -389,26 +443,26 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "### Scores\n",
+    "### <font color = 'gold'> Scores </font>\n",
     "\n",
-    "#### Histograms\n",
+    "#### <font color = 'turquoise'> Histograms </font>\n",
     "\n",
-    "Scores range from 0 to 100 and the median (77) is greater than the mean (74) suggesting that the distribution is left-skewed.  This is confirmed in the histogram and boxplot of scores below.  The distribution appears to be a normal (Gaussian) curve, but with a left-tail.  It would warrant further investigation to determine whether this is a true normal distribution or not.\n",
+    "Scores range from 0 to 100 with the median (77) greater than the mean (74) suggesting that the distribution is left-skewed.  This is confirmed in the histogram and boxplot of scores below.  The distribution appears to be a normal (Gaussian) curve, but with a left-tail.  \n",
     "\n",
-    "We can see that most students scored between 66 and 85.\n",
+    "Most students scored between 65 and 85.\n",
     "\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 396,
+   "execution_count": 34,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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",
       "text/plain": [
-       "<Figure size 1500x700 with 2 Axes>"
+       "<Figure size 1000x500 with 2 Axes>"
       ]
      },
      "metadata": {},
@@ -416,23 +470,21 @@
     }
    ],
    "source": [
-    "# set figure size (larger than default)\n",
-    "fig, axes = plt.subplots(figsize=(15, 7), sharex=True)\n",
+    "# set figure\n",
+    "fig, (ax1, ax2) = plt.subplots(ncols=2, figsize=(10, 5), )\n",
     "fig.suptitle('Histogram and Boxplot of \"Scores\"')\n",
-    "##fig.text(0.52, 0.0, 'score', ha='center')\n",
+    "\n",
     "# first subplot (matplotlib histogram)\n",
-    "plt.subplot(1,2,1).set_title('Histogram')\n",
-    "plt.hist(scores, bins=25, color='orange', ec = 'blue',  )\n",
-    "plt.subplot(1,2,1).set_xlabel('score')\n",
-    "plt.subplot(1,2,1).set_ylabel('Frequency')\n",
+    "ax1 = plt.subplot(121)\n",
+    "ax1.set_xlabel('score')\n",
+    "ax1.set_ylabel('Frequency')\n",
+    "ax1 = plt.hist(scores, bins=25, color='orange', ec = 'blue',  )\n",
     "\n",
-    "# second subplot (seaborn boxplot\n",
-    "plt.subplot(1,2,2)\n",
-    "sns.boxplot(x=scores).set_title('Boxplot')\n",
+    "# second subplot (seaborn boxplot)\n",
+    "sns.boxplot(x = scores,ax=ax2 )\n",
     "\n",
-    "# avoids overlapping subplots\n",
+    "# prevent overlapping of subplots\n",
     "plt.tight_layout()\n",
-    "\n",
     "plt.show()"
    ]
   },
@@ -441,17 +493,116 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "The dataset contains a few categorical variables - gender (male or female), age bands (0-35, 35-55, 55+) and disability declared (yes or no).  We can plot histograms for each subgroup to see if there are any differences in the distribution of scores between these subgroups.  A visual inspection of the histograms hints that female student score higher than male students, as do older students in comparison to the younger age bands, although there are very few students in the 55+ age band.  It is more difficult to see if there may be a different between students who have declared a disability and those who have not.  We will return to this later in the notebook."
+    "The dataset contains a few categorical variables - gender (male or female), age bands (0-35, 35-55, 55+) and disability declared (yes or no) - which can be used to divide the dataset into subgroups. \n",
+    "\n",
+    "We can plot histograms for each subgroup to see if there are any differences in the distribution of scores between these subgroups.  \n",
+    "\n",
+    "A visual inspection of the histograms hints that female students score higher than male students, as do older students in comparison to the younger age bands, although it is noticable that there are very few students in the 55+ age band.  \n",
+    "\n",
+    "It is more difficult to see if there is a difference between students who have declared a disability and those who have not.  \n",
+    "\n",
+    "I will return to this later in the notebook."
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#FIXME - deprecation warning"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 90,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "C:\\Users\\zoona\\AppData\\Local\\Temp\\ipykernel_35924\\3976228948.py:26: MatplotlibDeprecationWarning: Auto-removal of overlapping axes is deprecated since 3.6 and will be removed two minor releases later; explicitly call ax.remove() as needed.\n",
+      "  ax3 = plt.subplot(212)\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<Figure size 640x480 with 0 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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+      "text/plain": [
+       "<Figure size 1500x1000 with 3 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# set figure\n",
+    "plt.clf()\n",
+    "\n",
+    "fig, ((ax1, ax2), (ax3, ax4)) = plt.subplots(nrows = 2, ncols=2,figsize=(15, 10),  )\n",
+    "fig.suptitle('Histograms of \"Scores\" by categorical variables')\n",
+    "\n",
+    "\n",
+    "ax1 = plt.subplot(221)\n",
+    "ax1 = plt.hist(male['score'], bins=25,   alpha = 0.8, align='left', label = 'male', edgecolor='black', linewidth=1)\n",
+    "ax1 = plt.hist(female['score'], bins=25,   alpha = 0.3, align= 'right', label = 'female', edgecolor='red', linewidth=1)\n",
+    "ax1 = plt.title(' by Gender')\n",
+    "ax1 = plt.subplot(221).set_xlabel('Score')\n",
+    "ax1 = plt.subplot(221).set_ylabel('Frequency')\n",
+    "ax1 = plt.legend()\n",
+    "\n",
+    "ax2 = plt.subplot(222)\n",
+    "\n",
+    "ax2 = plt.hist(disabled_yes['score'], bins=25,   alpha = 0.8, align='left', label = 'disabled', edgecolor='black', linewidth=1)\n",
+    "ax2 = plt.hist(disabled_no['score'], bins=25,   alpha = 0.3, align= 'right', label = 'not disabled', edgecolor='red', linewidth=1)\n",
+    "ax2 = plt.title(' by Disability')\n",
+    "ax2 = plt.subplot(2,2,2).set_xlabel('Score')\n",
+    "ax2 = plt.subplot(2,2,2).set_ylabel('Frequency')\n",
+    "ax2 = plt.legend()\n",
+    "\n",
+    "# \n",
+    "ax3 = plt.subplot(212)\n",
+    "ax3 = plt.hist(age_0_35['score'], bins=25,   alpha = 0.8, align='left', label = 'age 0-35', edgecolor='black', linewidth=1)\n",
+    "ax3 = plt.hist(age_35_55['score'], bins=25,   alpha = 0.5, align= 'mid', label = 'age 35-55', edgecolor='red', linewidth=1)\n",
+    "ax3 = plt.hist(age_55_['score'], bins=25,   alpha = 0.5, align= 'left', label = 'age 55<=', edgecolor='green', linewidth=1)\n",
+    "ax3 = plt.title(' by Age Bands')\n",
+    "\n",
+    "\n",
+    "\n",
+    "ax3 = plt.legend()\n",
+    "plt.tight_layout()\n",
+    "\n",
+    "plt.show()"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 429,
+   "execution_count": 35,
    "metadata": {},
    "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "C:\\Users\\zoona\\AppData\\Local\\Temp\\ipykernel_35924\\2584693153.py:8: MatplotlibDeprecationWarning: Auto-removal of overlapping axes is deprecated since 3.6 and will be removed two minor releases later; explicitly call ax.remove() as needed.\n",
+      "  plt.subplot(2,2,1)\n",
+      "C:\\Users\\zoona\\AppData\\Local\\Temp\\ipykernel_35924\\2584693153.py:17: MatplotlibDeprecationWarning: Auto-removal of overlapping axes is deprecated since 3.6 and will be removed two minor releases later; explicitly call ax.remove() as needed.\n",
+      "  plt.subplot(2,2,2)\n"
+     ]
+    },
     {
      "data": {
-      "image/png": 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       "text/plain": [
        "<Figure size 1500x1200 with 2 Axes>"
       ]
@@ -495,7 +646,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 440,
+   "execution_count": 28,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -507,23 +658,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 441,
+   "execution_count": 31,
    "metadata": {},
    "outputs": [
-    {
-     "ename": "TypeError",
-     "evalue": "ylabel() missing 1 required positional argument: 'ylabel'",
-     "output_type": "error",
-     "traceback": [
-      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
-      "\u001b[1;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
-      "Cell \u001b[1;32mIn [441], line 7\u001b[0m\n\u001b[0;32m      5\u001b[0m plt\u001b[39m.\u001b[39mtitle(\u001b[39m'\u001b[39m\u001b[39m by Age Bands\u001b[39m\u001b[39m'\u001b[39m)\n\u001b[0;32m      6\u001b[0m \u001b[39m#plt.plot.set_xlabel('Score')\u001b[39;00m\n\u001b[1;32m----> 7\u001b[0m plt\u001b[39m.\u001b[39;49mylabel()\n\u001b[0;32m      8\u001b[0m plt\u001b[39m.\u001b[39mlegend()\n\u001b[0;32m     10\u001b[0m plt\u001b[39m.\u001b[39mshow()\n",
-      "\u001b[1;31mTypeError\u001b[0m: ylabel() missing 1 required positional argument: 'ylabel'"
-     ]
-    },
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "<Figure size 640x480 with 1 Axes>"
       ]
@@ -539,7 +679,7 @@
     "plt.hist(age_55_['score'], bins=25,   alpha = 0.5, align= 'left', label = 'age 55<=', edgecolor='green', linewidth=1)\n",
     "plt.title(' by Age Bands')\n",
     "#plt.plot.set_xlabel('Score')\n",
-    "plt.ylabel()\n",
+    "plt.xlabel('score')\n",
     "plt.legend()\n",
     "\n",
     "plt.show()\n",
diff --git a/Task2-ALL.ipynb b/Task2-ALL.ipynb
index 0383523..a4bb9a0 100644
--- a/Task2-ALL.ipynb
+++ b/Task2-ALL.ipynb
@@ -390,6 +390,53 @@
     "plt.show()"
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "C:\\Users\\zoona\\AppData\\Local\\Temp\\ipykernel_35924\\2936140974.py:6: MatplotlibDeprecationWarning: Auto-removal of overlapping axes is deprecated since 3.6 and will be removed two minor releases later; explicitly call ax.remove() as needed.\n",
+      "  plt.subplot(1,2,1)\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1000x500 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# set figure size (larger than default)\n",
+    "fig, axes = plt.subplots(2,1,figsize=(10, 5), sharex=True, sharey=True)\n",
+    "fig.suptitle('Histogram and Boxplot of \"Scores\"')\n",
+    "\n",
+    "# first subplot (matplotlib histogram)\n",
+    "plt.subplot(1,2,1)\n",
+    "plt.hist(scores, bins=25, color='orange', ec = 'blue',  )\n",
+    "plt.title('Histogram')\n",
+    "plt.xlabel('score')\n",
+    "plt.ylabel('Frequency')\n",
+    "\n",
+    "# second subplot (seaborn boxplot)\n",
+    "plt.subplot(1,2,2)\n",
+    "sns.boxplot(x=scores)\n",
+    "plt.title('Boxplot')\n",
+    "\n",
+    "# avoids overlapping subplots\n",
+    "plt.tight_layout()\n",
+    "\n",
+    "plt.show()"
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": 319,
@@ -1900,7 +1947,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.11.0"
+   "version": "3.11.0 (main, Oct 24 2022, 18:26:48) [MSC v.1933 64 bit (AMD64)]"
   },
   "orig_nbformat": 4,
   "vscode": {
-- 
GitLab