PYTHR/deployment/content/unit-6-pandas/instructor-resources/hw-10wk-13d/pandas_plotting_hw.ipynb

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    "<img src=\"http://imgur.com/1ZcRyrc.png\" style=\"float: left; margin: 20px; height: 55px\">\n",
    "\n",
    "## Homework: Plotting With Pandas\n",
    "\n",
    "_Authors: Kevin Coyle (L.A.)_\n",
    "\n",
    "---\n",
    "\n"
   ]
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   "source": [
    "Welcome!"
   ]
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   "source": [
    "#### Pandas: Plotting Practice Problems\n",
    "\n",
    "In this homework, you're going to write code for a few problems. \n",
    "\n",
    "You'll practice the following programming concepts we've covered in class:\n",
    "* Plotting with Pandas.\n",
    "* Determining best plot given a data set."
   ]
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   "source": [
    "#### #1. Import Pandas, `Matplotlib.pyplot`, and NumPy. Don't forget the line that makes `matplotlib` render in a Jupyter Notebook!"
   ]
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   "source": [
    "#### #2. Read in the NBA players `csv` into a variable called `nba_df`."
   ]
  },
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   "source": [
    "This is a data set of NBA players from 2015. The filename is `NBA_players_2015.csv`."
   ]
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   "source": [
    "#### #3. Look at the first five rows of the data set."
   ]
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   "source": [
    "#### #4. Create a histogram of the `age` column."
   ]
  },
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   "source": [
    "#### #5. Create a histogram of the `age` column, but change the number of bins to `20`."
   ]
  },
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   "source": [
    "#### #6. Discuss the difference in the two plots and the implications."
   ]
  },
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   "source": [
    "While skewed, the plot with fewer bins leads one to believe that the bin to the right of the highest-numbered age bin is the second largest. The second-largest bin occurs right after 22 and before 25."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### #7. Rename the `position` column `'pos'` with the following `C:5`, `G:1`, and `F:3`. Then create a scatter matrix plot with the `'pos'`, `'pts'`, `'age'`, and `'fg'` columns."
   ]
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   "source": [
    "#### #8. Plot the number of guards, centers, and forwards in this data set."
   ]
  },
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   "source": [
    "The end!"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Great job!"
   ]
  }
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