Pandas/Pandas-motm.ipynb

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    "# Pandas\n",
    "\n",
    "## CLEPY - August Module of the month\n",
    "### Anurag Saxena\n",
    "### [@_asaxena](https://twitter.com/_asaxena)"
   ]
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   "source": [
    "### Pandas - Python Data Analysis Library\n",
    "#### [pandas.pydata.org](http://pandas.pydata.org)"
   ]
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   "source": [
    "### Open Source"
   ]
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   "source": [
    "### High Performance"
   ]
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   },
   "source": [
    "### Easy to use Data Structures and Data Analysis Tools"
   ]
  },
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   "source": [
    "import pandas as pd\n",
    "import numpy as np"
   ]
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   "source": [
    "### There are two data structures in pandas that you will encounter the most: \n",
    "\n",
    "### - Series\n",
    "### - DataFrame\n",
    "\n"
   ]
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   "source": [
    "### Series\n",
    "Nothing but a one dimensional array like object containing an array of data"
   ]
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   "source": [
    "obj = pd.Series([1,3,4,5,6,7,8,9])\n",
    "obj"
   ]
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   "source": [
    "# You can define a custom index for series data\n",
    "\n",
    "obj_c = pd.Series([1,2,4], index=['a','b','c'])\n",
    "obj_c"
   ]
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   "source": [
    "### DataFrame\n",
    "\n",
    "Represents a tabular, spreadsheet like data sturcture.\n",
    "\n",
    "It has both row and column index. \n",
    "\n",
    "You can think about it as a dict of Series but sharing the same index.\n",
    "\n",
    "If you work with pandas, you are mostly working in dataframes."
   ]
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   "source": [
    "df_1 = pd.DataFrame(np.random.randint(0,100,size=(100,4)), columns=list('ABCD'))\n",
    "\n",
    "print(type(df_1))\n",
    "df_1.head()"
   ]
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   "source": [
    "df_1.tail(3)"
   ]
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   "source": [
    "df_1.index"
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    "df_1.columns"
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   "source": [
    "df_1.values"
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   "source": [
    "df_1.describe()"
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    "df_1.mean()"
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    "df_1"
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   "source": [
    "df_1['A']"
   ]
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   "source": [
    "df_1[0:3]"
   ]
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   "source": [
    "### Reading data from File\n",
    "\n",
    "Pandas can read and write to a multitude of [file formats](https://pandas.pydata.org/pandas-docs/stable/io.html)\n"
   ]
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   "source": [
    "import time\n",
    "t_start = time.time()\n",
    "apple_df = pd.read_csv('https://raw.githubusercontent.com/matplotlib/sample_data/master/aapl.csv')\n",
    "t_end = time.time()\n",
    "print (t_end - t_start)\n",
    "apple_df.head()"
   ]
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   "source": [
    "apple_describe_time_start = time.time()\n",
    "apple_df.describe()\n",
    "apple_describe_time_end = time.time()"
   ]
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   "outputs": [],
   "source": [
    "print (apple_describe_time_end - apple_describe_time_start)"
   ]
  },
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   "source": [
    "# pd.set_eng_float_format(accuracy=2, use_eng_prefix=True)\n",
    "apple_df.mean()\n"
   ]
  },
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   "source": [
    "### Checkout 10 minute to pandas to get started quickly\n",
    "\n",
    "[https://pandas.pydata.org/pandas-docs/stable/10min.html](https://pandas.pydata.org/pandas-docs/stable/10min.html)"
   ]
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   },
   "source": [
    "### Book to checkout if you want to work on this more\n",
    "\n",
    "![python for data analysis wes mckinney](http://t3.gstatic.com/images?q=tbn:ANd9GcQ0SUQ5II7tUi0qGpSDbzTG6CyYbOFat3WMtCc1-kYTz5scCT9R)"
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   "source": [
    "## Thanks\n",
    "\n",
    "\n",
    "## Questions?"
   ]
  }
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