Chapter 12 - Reading from a CSV.ipynb

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    "# Render our plots inline\n",
    "%matplotlib inline\n",
    "\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# Make the graphs a bit prettier, and bigger\n",
    "plt.style.use('ggplot')\n",
    "plt.rcParams['figure.figsize'] = (15, 5)"
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   "source": [
    "# 1.1 Reading data from a csv file"
   ]
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    "You can read data from a CSV file using the `read_csv` function. By default, it assumes that the fields are comma-separated.\n",
    "\n",
    "We're going to be looking some cyclist data from Montréal. Here's the [original page](http://donnees.ville.montreal.qc.ca/dataset/velos-comptage) (in French), but it's already included in this repository. We're using the data from 2012.\n",
    "\n",
    "This dataset is a list of how many people were on 7 different bike paths in Montreal, each day."
   ]
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   "source": [
    "broken_df = pd.read_csv('data/bikes.csv',encoding = \"ISO-8859-1\")"
   ]
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    "# Look at the first 3 rows\n",
    "broken_df[:3]"
   ]
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    "You'll notice that this is totally broken! `read_csv` has a bunch of options that will let us fix that, though. Here we'll\n",
    "\n",
    "* change the column separator to a `;`\n",
    "* Set the encoding to `'latin1'` (the default is `'utf8'`)\n",
    "* Parse the dates in the 'Date' column\n",
    "* Tell it that our dates have the day first instead of the month first\n",
    "* Set the index to be the 'Date' column"
   ]
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    "fixed_df = pd.read_csv('data/bikes.csv', sep=';', encoding='latin1', parse_dates=['Date'], dayfirst=True, index_col='Date')\n",
    "fixed_df[:3]"
   ]
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   "source": [
    "# 1.2 Selecting a column"
   ]
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    "When you read a CSV, you get a kind of object called a `DataFrame`, which is made up of rows and columns. You get columns out of a DataFrame the same way you get elements out of a dictionary.\n",
    "\n",
    "Here's an example:"
   ]
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    "fixed_df['Berri 1']"
   ]
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   "source": [
    "# 1.3 Plotting a column"
   ]
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    "Just add `.plot()` to the end! How could it be easier? =)\n",
    "\n",
    "We can see that, unsurprisingly, not many people are biking in January, February, and March, "
   ]
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  {
   "cell_type": "code",
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   "source": [
    "fixed_df['Berri 1'].plot()"
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    "We can also plot all the columns just as easily. We'll make it a little bigger, too.\n",
    "You can see that it's more squished together, but all the bike paths behave basically the same -- if it's a bad day for cyclists, it's a bad day everywhere."
   ]
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    "fixed_df.plot(figsize=(15, 10))"
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   "source": [
    "# 1.4 Putting all that together"
   ]
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   "cell_type": "markdown",
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   "source": [
    "Here's the code we needed to write do draw that graph, all together:"
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    "df = pd.read_csv('data/bikes.csv', sep=';', encoding='latin1', parse_dates=['Date'], dayfirst=True, index_col='Date')\n",
    "df['Berri 1'].plot()"
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