"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "plt.bar(frequency.index, frequency.values)\n",
+ "plt.xlabel(\"Dice Value\")\n",
+ "plt.ylabel(\"Frequency\")\n",
+ "plt.title(\"Frequency Distribution of Dice Rolling Simulation\")\n",
+ "plt.xticks(frequency.index)\n",
+ "plt.show()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "\"\"\"\n",
+ "The 1st plot shows us the results of rolling the dice 10 times, sorted by their values. \n",
+ "Each bar represents a roll, and the height of the bar corresponds to the value rolled. \n",
+ "This plot provides a visual representation of the individual outcomes of each roll.\n",
+ "\n",
+ "The 2nd plot, which represents the frequency distribution, displays the number of occurrences (frequency) for each possible dice value. \n",
+ "Each bar in this plot represents one of the six possible dice values, and the height of the bar indicates how many times that value appeared in the 10 rolls.\n",
+ "\n",
+ "\"\"\""
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Challenge 2\n",
+ "Now, using the dice results obtained in *challenge 1*, your are going to define some functions that will help you calculate the mean of your data in two different ways, the median and the four quartiles. \n",
+ "\n",
+ "#### 1.- Define a function that computes the mean by summing all the observations and dividing by the total number of observations. You are not allowed to use any methods or functions that directly calculate the mean value. "
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 37,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "3.9"
+ ]
+ },
+ "execution_count": 37,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "def mean_dice(data):\n",
+ " mean = data.sum() / len(data)\n",
+ " return mean\n",
+ "\n",
+ "result_column = dice_df['Results']\n",
+ "mean_value = mean_dice(result_column)\n",
+ "mean_value"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "#### 2.- First, calculate the frequency distribution. Then, calculate the mean using the values of the frequency distribution you've just computed. You are not allowed to use any methods or functions that directly calculate the mean value. "
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 38,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Frequency Distribution:\n",
+ "6 3\n",
+ "5 2\n",
+ "1 2\n",
+ "4 1\n",
+ "2 1\n",
+ "3 1\n",
+ "Name: Results, dtype: int64\n",
+ "The mean is 3.9\n"
+ ]
+ }
+ ],
+ "source": [
+ "freq_distribution = dice_df[\"Results\"].value_counts()\n",
+ "sum_values = 0\n",
+ "total_count = 0\n",
+ "\n",
+ "for value, count in freq_distribution.items():\n",
+ " sum_values += value * count\n",
+ " total_count += count\n",
+ "\n",
+ "mean_value = sum_values / total_count\n",
+ "\n",
+ "print(\"Frequency Distribution:\")\n",
+ "print(freq_distribution)\n",
+ "print(f\"The mean is {mean_value}\")"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "#### 3.- Define a function to calculate the median. You are not allowed to use any methods or functions that directly calculate the median value. \n",
+ "**Hint**: you might need to define two computation cases depending on the number of observations used to calculate the median."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 53,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "def median_dice(data):\n",
+ " sorted_data = sorted(data)\n",
+ " n = len(sorted_data)\n",
+ " \n",
+ " if n % 2 == 1:\n",
+ " median_index = n // 2\n",
+ " median_value = sorted_data[median_index]\n",
+ " else:\n",
+ " upper_median_index = n // 2\n",
+ " lower_median_index = upper_median_index - 1\n",
+ " median_value = (sorted_data[lower_median_index] + sorted_data[upper_median_index]) / 2.0\n",
+ " \n",
+ " return median_value"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 54,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "The median is 4.5\n"
+ ]
+ }
+ ],
+ "source": [
+ "median_dice = median_dice(dice_df[\"Results\"])\n",
+ "\n",
+ "print(f\"The median is {median_dice}\")"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "#### 4.- Define a function to calculate the four quartiles. You can use the function you defined above to compute the median but you are not allowed to use any methods or functions that directly calculate the quartiles. "
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 56,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "The first quartile is 2\n",
+ "The second quartile is 4.5\n",
+ "The third quartile is 6\n",
+ "The fourth quartile is 6\n"
+ ]
+ }
+ ],
+ "source": [
+ "def median_dice(data):\n",
+ " sorted_data = sorted(data)\n",
+ " n = len(sorted_data)\n",
+ " \n",
+ " if n % 2 == 1:\n",
+ " median_index = n // 2\n",
+ " median_value = sorted_data[median_index]\n",
+ " else:\n",
+ " upper_median_index = n // 2\n",
+ " lower_median_index = upper_median_index - 1\n",
+ " median_value = (sorted_data[lower_median_index] + sorted_data[upper_median_index]) / 2.0\n",
+ " \n",
+ " return median_value\n",
+ "\n",
+ "\n",
+ "def quartiles_dice(data):\n",
+ " sorted_data = sorted(data)\n",
+ " n = len(sorted_data)\n",
+ " \n",
+ " q1 = median_dice(sorted_data[:n // 2])\n",
+ " q2 = median_dice(sorted_data)\n",
+ " q3 = median_dice(sorted_data[n // 2:]) if n % 2 == 0 else median_dice(sorted_data[n // 2 + 1:])\n",
+ " q4 = sorted_data[-1] \n",
+ " return q1, q2, q3, q4\n",
+ "\n",
+ "\n",
+ "result_column = dice_df[\"Results\"]\n",
+ "q1, q2, q3, q4 = quartiles_dice(result_column)\n",
+ "\n",
+ "print(f\"The first quartile is {q1}\")\n",
+ "print(f\"The second quartile is {q2}\")\n",
+ "print(f\"The third quartile is {q3}\")\n",
+ "print(f\"The fourth quartile is {q4}\")"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Challenge 3\n",
+ "Read the csv `roll_the_dice_hundred.csv` from the `data` folder.\n",
+ "#### 1.- Sort the values and plot them. What do you see?"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 63,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
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+ "source": [
+ "data = pd.read_csv(\"roll_the_dice_hundred.csv\")\n",
+ "data"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 66,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "C:\\Users\\USER\\AppData\\Local\\Temp\\ipykernel_14744\\2676058639.py:3: UserWarning: \n",
+ "\n",
+ "`distplot` is a deprecated function and will be removed in seaborn v0.14.0.\n",
+ "\n",
+ "Please adapt your code to use either `displot` (a figure-level function with\n",
+ "similar flexibility) or `histplot` (an axes-level function for histograms).\n",
+ "\n",
+ "For a guide to updating your code to use the new functions, please see\n",
+ "https://gist.github.com/mwaskom/de44147ed2974457ad6372750bbe5751\n",
+ "\n",
+ " sns.distplot(sorted_values)\n"
+ ]
+ },
+ {
+ "data": {
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\n",
+ "text/plain": [
+ "
"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "plt.hist(data[\"value\"])"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "\"\"\"\n",
+ "In the dataset, we observe that the values 6, 4, and 2 have the highest frequencies, with occurrences of 23, 22, and 17 respectively. \n",
+ "The mean value across the dataset is calculated 3.74. \n",
+ "This mean, along with the distribution characteristics, suggests that the data is slightly skewed towards higher values, indicating a weak positive skewness.\n",
+ "\"\"\""
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "#### 5.- Read the `roll_the_dice_thousand.csv` from the `data` folder. Plot the frequency distribution as you did before. Has anything changed? Why do you think it changed?"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 87,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "1 175\n",
+ "3 175\n",
+ "4 168\n",
+ "2 167\n",
+ "6 166\n",
+ "5 149\n",
+ "Name: value, dtype: int64\n"
+ ]
+ },
+ {
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ "
"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "data = pd.read_csv(\"roll_the_dice_thousand.csv\")\n",
+ "data\n",
+ "\n",
+ "freq_distribution = data[\"value\"].value_counts()\n",
+ "print(freq_distribution)\n",
+ "\n",
+ "plt.bar(freq_distribution.index, freq_distribution.values)\n",
+ "plt.xlabel(\"Value\")\n",
+ "plt.ylabel(\"Frequency\")\n",
+ "plt.show()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "\"\"\"\n",
+ "The frequency change because we add more values \n",
+ "\"\"\""
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Challenge 4\n",
+ "In the `data` folder of this repository you will find three different files with the prefix `ages_population`. These files contain information about a poll answered by a thousand people regarding their age. Each file corresponds to the poll answers in different neighbourhoods of Barcelona.\n",
+ "\n",
+ "#### 1.- Read the file `ages_population.csv`. Calculate the frequency distribution and plot it as we did during the lesson. Try to guess the range in which the mean and the standard deviation will be by looking at the plot. "
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 89,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ "
"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "ages_population = pd.read_csv(\"ages_population.csv\")\n",
+ "ages_population\n",
+ "\n",
+ "freq_distribution = ages_population[\"observation\"].value_counts()\n",
+ "\n",
+ "plt.bar(freq_distribution.index, freq_distribution.values)\n",
+ "plt.xlabel(\"Age\")\n",
+ "plt.ylabel(\"Frequency\")\n",
+ "plt.show()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "#### 2.- Calculate the exact mean and standard deviation and compare them with your guesses. Do they fall inside the ranges you guessed?"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 90,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "The mean is 36.56\n",
+ "The standard deviation is 12.816499625976762\n"
+ ]
+ }
+ ],
+ "source": [
+ "\n",
+ "mean = ages_population[\"observation\"].mean()\n",
+ "print(f\"The mean is {mean}\")\n",
+ "\n",
+ "std_dev = ages_population[\"observation\"].std()\n",
+ "print(f\"The standard deviation is {std_dev}\")"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "\"\"\"\n",
+ "your comments here\n",
+ "\"\"\""
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "#### 3.- Now read the file `ages_population2.csv` . Calculate the frequency distribution and plot it."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 91,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ "
"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "ages_population_2 = pd.read_csv(\"ages_population2.csv\")\n",
+ "ages_population_2\n",
+ "\n",
+ "freq_distribution = ages_population_2[\"observation\"].value_counts()\n",
+ "\n",
+ "plt.bar(freq_distribution.index, freq_distribution.values)\n",
+ "plt.xlabel(\"Age\")\n",
+ "plt.ylabel(\"Frequency\")\n",
+ "plt.show()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "#### 4.- What do you see? Is there any difference with the frequency distribution in step 1?"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "\"\"\"\n",
+ "Yes, there are difference\n",
+ "\n",
+ "\"\"\""
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "#### 5.- Calculate the mean and standard deviation. Compare the results with the mean and standard deviation in step 2. What do you think?"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 94,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "The mean for population 2 is 27.155\n",
+ "The standard deviation for population 2 is 2.969813932689186\n",
+ "The mean for population is 36.56\n",
+ "The standard deviation for population is 12.816499625976762\n"
+ ]
+ }
+ ],
+ "source": [
+ "mean = ages_population_2[\"observation\"].mean()\n",
+ "print(f\"The mean for population 2 is {mean}\")\n",
+ "\n",
+ "std_dev = ages_population_2[\"observation\"].std()\n",
+ "print(f\"The standard deviation for population 2 is {std_dev}\")\n",
+ "\n",
+ "mean = ages_population[\"observation\"].mean()\n",
+ "print(f\"The mean for population is {mean}\")\n",
+ "\n",
+ "std_dev = ages_population[\"observation\"].std()\n",
+ "print(f\"The standard deviation for population is {std_dev}\")\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": []
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "\"\"\"\n",
+ "The overall pattern of the distribution remains consistent, although the dataset comprises distinct observation values.\n",
+ "\"\"\""
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Challenge 5\n",
+ "Now is the turn of `ages_population3.csv`.\n",
+ "\n",
+ "#### 1.- Read the file `ages_population3.csv`. Calculate the frequency distribution and plot it."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 99,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ "
"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "ages_population_3 = pd.read_csv(\"ages_population3.csv\")\n",
+ "ages_population_3\n",
+ "\n",
+ "freq_distribution = ages_population_3[\"observation\"].value_counts()\n",
+ "\n",
+ "plt.bar(freq_distribution.index, freq_distribution.values)\n",
+ "plt.xlabel(\"Age\")\n",
+ "plt.ylabel(\"Frequency\")\n",
+ "plt.show()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "#### 2.- Calculate the mean and standard deviation. Compare the results with the plot in step 1. What is happening?"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 100,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "The mean for population 3 is 41.989\n",
+ "The standard deviation for population 3 is 16.144705959865934\n",
+ "The mean for population 1 is 36.56\n",
+ "The standard deviation for population 1 is 12.816499625976762\n"
+ ]
+ }
+ ],
+ "source": [
+ "mean = ages_population_3[\"observation\"].mean()\n",
+ "print(f\"The mean for population 3 is {mean}\")\n",
+ "\n",
+ "std_dev = ages_population_3[\"observation\"].std()\n",
+ "print(f\"The standard deviation for population 3 is {std_dev}\")\n",
+ "\n",
+ "mean = ages_population[\"observation\"].mean()\n",
+ "print(f\"The mean for population 1 is {mean}\")\n",
+ "\n",
+ "std_dev = ages_population[\"observation\"].std()\n",
+ "print(f\"The standard deviation for population 1 is {std_dev}\")"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "\"\"\"\n",
+ "The mean for population 3 (41.989) is higher than the mean for population 1 (36.56).\n",
+ "This suggests that, on average, the values in population 3 tend to be larger than those in population 1.\n",
+ "\n",
+ "The standard deviation for population 3 (16.144705959865934) is larger than the standard deviation for population 1 (12.816499625976762). \n",
+ "A larger standard deviation indicates that the values in population 3 are more spread out from the mean compared to those in population 1.\n",
+ "\"\"\""
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "#### 3.- Calculate the four quartiles. Use the results to explain your reasoning for question in step 2. How much of a difference is there between the median and the mean?"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 105,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "The first quartile is 30.0\n",
+ "The second quartile is 40.0\n",
+ "The third quartile is 53.0\n",
+ "The fourth quartile is 77.0\n"
+ ]
+ }
+ ],
+ "source": [
+ "q1 = np.quantile(ages_population_3, 0.25)\n",
+ "print(f\"The first quartile is {q1}\")\n",
+ "q2 = np.quantile(ages_population_3, 0.50)\n",
+ "print(f\"The second quartile is {q2}\")\n",
+ "q3 = np.quantile(ages_population_3, 0.75)\n",
+ "print(f\"The third quartile is {q3}\")\n",
+ "q4 = np.quantile(ages_population_3, 1)\n",
+ "print(f\"The fourth quartile is {q4}\")"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 106,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "
\n",
+ "\n",
+ "
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+ " \n",
+ "
\n",
+ "
\n",
+ "
observation
\n",
+ "
\n",
+ " \n",
+ " \n",
+ "
\n",
+ "
count
\n",
+ "
1000.000000
\n",
+ "
\n",
+ "
\n",
+ "
mean
\n",
+ "
41.989000
\n",
+ "
\n",
+ "
\n",
+ "
std
\n",
+ "
16.144706
\n",
+ "
\n",
+ "
\n",
+ "
min
\n",
+ "
1.000000
\n",
+ "
\n",
+ "
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+ "
25%
\n",
+ "
30.000000
\n",
+ "
\n",
+ "
\n",
+ "
50%
\n",
+ "
40.000000
\n",
+ "
\n",
+ "
\n",
+ "
75%
\n",
+ "
53.000000
\n",
+ "
\n",
+ "
\n",
+ "
max
\n",
+ "
77.000000
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+ "
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+ " \n",
+ "
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+ "
"
+ ],
+ "text/plain": [
+ " observation\n",
+ "count 1000.000000\n",
+ "mean 41.989000\n",
+ "std 16.144706\n",
+ "min 1.000000\n",
+ "25% 30.000000\n",
+ "50% 40.000000\n",
+ "75% 53.000000\n",
+ "max 77.000000"
+ ]
+ },
+ "execution_count": 106,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "ages_population_3.describe()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 107,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "observation 40.0\n",
+ "dtype: float64"
+ ]
+ },
+ "execution_count": 107,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "ages_population_3.median()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 109,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "observation 0.210389\n",
+ "dtype: float64"
+ ]
+ },
+ "execution_count": 109,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "ages_population_3.skew()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "\"\"\"\n",
+ "The median and the mean exhibit minimal disparity, indicating that the distribution is nearly balanced around a central point.\n",
+ "\"\"\""
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "#### 4.- Calculate other percentiles that might be useful to give more arguments to your reasoning."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 110,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "The 10th percentile is 22.0\n",
+ "The 90th percentile is 67.0\n"
+ ]
+ }
+ ],
+ "source": [
+ "p10 = np.percentile(ages_population_3, 10)\n",
+ "p90 = np.percentile(ages_population_3, 90)\n",
+ "\n",
+ "print(f\"The 10th percentile is {p10}\")\n",
+ "print(f\"The 90th percentile is {p90}\")"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "\"\"\"\n",
+ "The 10th percentile (p10) represents the value below which 10% of the data falls. \n",
+ "It provides an idea of the lower range of ages in your dataset.\n",
+ "The 90th percentile (p90) represents the value below which 90% of the data falls. \n",
+ "It gives insight into the upper range of ages in your dataset.\n",
+ "\"\"\""
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Bonus challenge\n",
+ "Compare the information about the three neighbourhoods. Prepare a report about the three of them. Remember to find out which are their similarities and their differences backing your arguments in basic statistics."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 113,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
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+ "text/plain": [
+ "
"
+ ],
+ "text/plain": [
+ " Population 1 Population 2 Population 3\n",
+ "count 1000.0000 1000.000000 1000.000000\n",
+ "mean 36.5600 27.155000 41.989000\n",
+ "std 12.8165 2.969814 16.144706\n",
+ "min 1.0000 19.000000 1.000000\n",
+ "25% 28.0000 25.000000 30.000000\n",
+ "50% 37.0000 27.000000 40.000000\n",
+ "75% 45.0000 29.000000 53.000000\n",
+ "max 82.0000 36.000000 77.000000"
+ ]
+ },
+ "execution_count": 125,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "comparison_df = pd.concat([ages_population.describe(),\n",
+ " ages_population_2.describe(),\n",
+ " ages_population_3.describe()],\n",
+ " axis=1)\n",
+ "comparison_df.columns = ['Population 1', 'Population 2', 'Population 3']\n",
+ "comparison_df"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "\"\"\"\n",
+ "###Similarities:\n",
+ "##Count: All three neighborhoods have the same count of 1000 residents each.\n",
+ "##Central Tendency (Mean and Median):The mean population values differ across the neighborhoods, with Population 3 having the highest mean, followed by Population 1, and then Population 2. \n",
+ "However, the median population values are relatively close, with Population 1 having the highest median, followed by Population 3, and then Population 2.\n",
+ "##Interquartile Range (IQR): The IQR, represented by the difference between the 25th and 75th percentiles, varies among the neighborhoods, but the general range is somewhat consistent. Population 2 has the narrowest IQR, while Population 3 has the widest.\n",
+ "\n",
+ "###Differences:\n",
+ "##Population Range: Population 1 has the highest maximum population (82), while Population 2 has the lowest maximum population (36). Population 3 falls in between. This suggests that Population 1 is the most populous, followed by Population 3 and then Population 2.\n",
+ "##Spread (Standard Deviation): Population 3 has the highest standard deviation (16.144706), indicating greater variability or spread in its population distribution. \n",
+ "Population 2 has the lowest standard deviation, implying that its population is more clustered around the mean.\n",
+ "##Minimum Population: Population 1 and Population 3 have a minimum population of 1, while Population 2 has a minimum population of 19.\n",
+ "##Upper Quartile (75th Percentile): Population 3 has the highest upper quartile value (53), indicating that 75% of its population falls below this value. This suggests a larger upper segment of the population in Population 3 compared to the other two neighborhoods.\n",
+ "\n",
+ "###Conclusion:\n",
+ "\n",
+ "In conclusion, these three neighborhoods exhibit both similarities and differences in their population distributions. \n",
+ "While they have the same count and relatively similar median values, they differ in terms of their mean, spread, range, and upper quartile values. \n",
+ "\n",
+ "\n",
+ "In conclusion, the three populations (neighborhoods) exhibit differences in their central tendency, dispersion, distribution spread, and quartiles.\n",
+ "These statistics offer valuable insights into the demographic characteristics of these neighborhoods, which could be useful for decision-making and further analysis.\n",
+ "\n",
+ "\"\"\""
+ ]
+ }
+ ],
+ "metadata": {
+ "kernelspec": {
+ "display_name": "Python 3 (ipykernel)",
+ "language": "python",
+ "name": "python3"
+ },
+ "language_info": {
+ "codemirror_mode": {
+ "name": "ipython",
+ "version": 3
+ },
+ "file_extension": ".py",
+ "mimetype": "text/x-python",
+ "name": "python",
+ "nbconvert_exporter": "python",
+ "pygments_lexer": "ipython3",
+ "version": "3.10.9"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}