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Pandas / 02 - Learning Pandas / 01 - Starting with Pandas
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01 - Starting with Pandas.ipynb
{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Object Creation in Pandas"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
"import pandas as pd\n",
"import numpy as np"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Creating basic series"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"0 1.0\n",
"1 3.0\n",
"2 5.0\n",
"3 NaN\n",
"4 6.0\n",
"5 8.0\n",
"dtype: float64"
]
},
"execution_count": 2,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"my_series = pd.Series([1,3,5,np.nan,6,8])\n",
"my_series"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Create data frame as datetime index"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"DatetimeIndex(['2016-01-01', '2016-01-02', '2016-01-03', '2016-01-04',\n",
" '2016-01-05', '2016-01-06'],\n",
" dtype='datetime64[ns]', freq='D')"
]
},
"execution_count": 3,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"my_dates_index = pd.date_range('20160101', periods=6)\n",
"my_dates_index"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Create DataFrame object from python dictionary"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>float</th>\n",
" <th>time</th>\n",
" <th>series</th>\n",
" <th>array</th>\n",
" <th>categories</th>\n",
" <th>dull</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>1.0</td>\n",
" <td>2016-08-25</td>\n",
" <td>1.0</td>\n",
" <td>3</td>\n",
" <td>test</td>\n",
" <td>boring data</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>1.0</td>\n",
" <td>2016-08-25</td>\n",
" <td>1.0</td>\n",
" <td>3</td>\n",
" <td>train</td>\n",
" <td>boring data</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>1.0</td>\n",
" <td>2016-08-25</td>\n",
" <td>1.0</td>\n",
" <td>3</td>\n",
" <td>taxes</td>\n",
" <td>boring data</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>1.0</td>\n",
" <td>2016-08-25</td>\n",
" <td>1.0</td>\n",
" <td>3</td>\n",
" <td>tools</td>\n",
" <td>boring data</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" float time series array categories dull\n",
"0 1.0 2016-08-25 1.0 3 test boring data\n",
"1 1.0 2016-08-25 1.0 3 train boring data\n",
"2 1.0 2016-08-25 1.0 3 taxes boring data\n",
"3 1.0 2016-08-25 1.0 3 tools boring data"
]
},
"execution_count": 4,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df_from_dictionary = pd.DataFrame({ \n",
" 'float' : 1.,\n",
" 'time' : pd.Timestamp('20160825'),\n",
" 'series' : pd.Series(1,index=list(range(4)),dtype='float32'),\n",
" 'array' : np.array([3] * 4,dtype='int32'),\n",
" 'categories' : pd.Categorical([\"test\",\"train\",\"taxes\",\"tools\"]),\n",
" 'dull' : 'boring data' \n",
" })\n",
"df_from_dictionary"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Create DataFrame object Using Numpy array"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Original Numpy array\n",
"[[ 0 1 2 3]\n",
" [ 4 5 6 7]\n",
" [ 8 9 10 11]\n",
" [12 13 14 15]\n",
" [16 17 18 19]\n",
" [20 21 22 23]]\n",
"\n",
"\n",
"Origina Numpy array converted to Pandas dataframe\n"
]
},
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>0</th>\n",
" <th>1</th>\n",
" <th>2</th>\n",
" <th>3</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>2</td>\n",
" <td>3</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>4</td>\n",
" <td>5</td>\n",
" <td>6</td>\n",
" <td>7</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>8</td>\n",
" <td>9</td>\n",
" <td>10</td>\n",
" <td>11</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>12</td>\n",
" <td>13</td>\n",
" <td>14</td>\n",
" <td>15</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>16</td>\n",
" <td>17</td>\n",
" <td>18</td>\n",
" <td>19</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5</th>\n",
" <td>20</td>\n",
" <td>21</td>\n",
" <td>22</td>\n",
" <td>23</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" 0 1 2 3\n",
"0 0 1 2 3\n",
"1 4 5 6 7\n",
"2 8 9 10 11\n",
"3 12 13 14 15\n",
"4 16 17 18 19\n",
"5 20 21 22 23"
]
},
"execution_count": 5,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"sample_numpy_data = np.array(np.arange(24)).reshape((6,4))\n",
"print(\"Original Numpy array\")\n",
"print(sample_numpy_data)\n",
"print(\"\\n\")\n",
"sample_numpy_df = pd.DataFrame(sample_numpy_data)\n",
"print(\"Origina Numpy array converted to Pandas dataframe\")\n",
"sample_numpy_df"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Create DataFrame object using numpy array as values and datetime array as index\n",
"**(for clarity purpose we have added name index to columns)**"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>A</th>\n",
" <th>B</th>\n",
" <th>C</th>\n",
" <th>D</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>2016-01-01</th>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>2</td>\n",
" <td>3</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2016-01-02</th>\n",
" <td>4</td>\n",
" <td>5</td>\n",
" <td>6</td>\n",
" <td>7</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2016-01-03</th>\n",
" <td>8</td>\n",
" <td>9</td>\n",
" <td>10</td>\n",
" <td>11</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2016-01-04</th>\n",
" <td>12</td>\n",
" <td>13</td>\n",
" <td>14</td>\n",
" <td>15</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2016-01-05</th>\n",
" <td>16</td>\n",
" <td>17</td>\n",
" <td>18</td>\n",
" <td>19</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2016-01-06</th>\n",
" <td>20</td>\n",
" <td>21</td>\n",
" <td>22</td>\n",
" <td>23</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" A B C D\n",
"2016-01-01 0 1 2 3\n",
"2016-01-02 4 5 6 7\n",
"2016-01-03 8 9 10 11\n",
"2016-01-04 12 13 14 15\n",
"2016-01-05 16 17 18 19\n",
"2016-01-06 20 21 22 23"
]
},
"execution_count": 6,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"sample_df = pd.DataFrame(sample_numpy_data, index=my_dates_index, columns=list('ABCD'))\n",
"sample_df"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Basic Operations on Pandas DataFrame or Series Object"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### display first 5 rows of dataframe object using head method\n",
"**by default the head method shows the first five rows, you can also display the exact number of rows by passing and integer value to the head method**"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>A</th>\n",
" <th>B</th>\n",
" <th>C</th>\n",
" <th>D</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>2016-01-01</th>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>2</td>\n",
" <td>3</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2016-01-02</th>\n",
" <td>4</td>\n",
" <td>5</td>\n",
" <td>6</td>\n",
" <td>7</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2016-01-03</th>\n",
" <td>8</td>\n",
" <td>9</td>\n",
" <td>10</td>\n",
" <td>11</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2016-01-04</th>\n",
" <td>12</td>\n",
" <td>13</td>\n",
" <td>14</td>\n",
" <td>15</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2016-01-05</th>\n",
" <td>16</td>\n",
" <td>17</td>\n",
" <td>18</td>\n",
" <td>19</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" A B C D\n",
"2016-01-01 0 1 2 3\n",
"2016-01-02 4 5 6 7\n",
"2016-01-03 8 9 10 11\n",
"2016-01-04 12 13 14 15\n",
"2016-01-05 16 17 18 19"
]
},
"execution_count": 7,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"sample_df.head()"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>A</th>\n",
" <th>B</th>\n",
" <th>C</th>\n",
" <th>D</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>2016-01-01</th>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>2</td>\n",
" <td>3</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2016-01-02</th>\n",
" <td>4</td>\n",
" <td>5</td>\n",
" <td>6</td>\n",
" <td>7</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2016-01-03</th>\n",
" <td>8</td>\n",
" <td>9</td>\n",
" <td>10</td>\n",
" <td>11</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" A B C D\n",
"2016-01-01 0 1 2 3\n",
"2016-01-02 4 5 6 7\n",
"2016-01-03 8 9 10 11"
]
},
"execution_count": 8,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"sample_df.head(3) # displays the first 3 rows of the dataframe object"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### display last 5 rows of dataframe object using tail method\n",
"**by default the tail method shows the last five rows, you can also display the exact number of rows by passing and integer value to the tail method**"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>A</th>\n",
" <th>B</th>\n",
" <th>C</th>\n",
" <th>D</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>2016-01-02</th>\n",
" <td>4</td>\n",
" <td>5</td>\n",
" <td>6</td>\n",
" <td>7</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2016-01-03</th>\n",
" <td>8</td>\n",
" <td>9</td>\n",
" <td>10</td>\n",
" <td>11</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2016-01-04</th>\n",
" <td>12</td>\n",
" <td>13</td>\n",
" <td>14</td>\n",
" <td>15</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2016-01-05</th>\n",
" <td>16</td>\n",
" <td>17</td>\n",
" <td>18</td>\n",
" <td>19</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2016-01-06</th>\n",
" <td>20</td>\n",
" <td>21</td>\n",
" <td>22</td>\n",
" <td>23</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" A B C D\n",
"2016-01-02 4 5 6 7\n",
"2016-01-03 8 9 10 11\n",
"2016-01-04 12 13 14 15\n",
"2016-01-05 16 17 18 19\n",
"2016-01-06 20 21 22 23"
]
},
"execution_count": 9,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"sample_df.tail()"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>A</th>\n",
" <th>B</th>\n",
" <th>C</th>\n",
" <th>D</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>2016-01-04</th>\n",
" <td>12</td>\n",
" <td>13</td>\n",
" <td>14</td>\n",
" <td>15</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2016-01-05</th>\n",
" <td>16</td>\n",
" <td>17</td>\n",
" <td>18</td>\n",
" <td>19</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2016-01-06</th>\n",
" <td>20</td>\n",
" <td>21</td>\n",
" <td>22</td>\n",
" <td>23</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" A B C D\n",
"2016-01-04 12 13 14 15\n",
"2016-01-05 16 17 18 19\n",
"2016-01-06 20 21 22 23"
]
},
"execution_count": 10,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"sample_df.tail(3) # displays the last 3 rows of the dataframe object"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### display data type of each column in dataframe object"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"float float64\n",
"time datetime64[ns]\n",
"series float32\n",
"array int32\n",
"categories category\n",
"dull object\n",
"dtype: object"
]
},
"execution_count": 11,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df_from_dictionary.dtypes"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### display only the values of dataframe object"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"array([[ 0, 1, 2, 3],\n",
" [ 4, 5, 6, 7],\n",
" [ 8, 9, 10, 11],\n",
" [12, 13, 14, 15],\n",
" [16, 17, 18, 19],\n",
" [20, 21, 22, 23]])"
]
},
"execution_count": 12,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"sample_df.values"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"array([ 1., 3., 5., nan, 6., 8.])"
]
},
"execution_count": 13,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# in case of series object\n",
"my_series.values"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"array([[1.0, Timestamp('2016-08-25 00:00:00'), 1.0, 3, 'test',\n",
" 'boring data'],\n",
" [1.0, Timestamp('2016-08-25 00:00:00'), 1.0, 3, 'train',\n",
" 'boring data'],\n",
" [1.0, Timestamp('2016-08-25 00:00:00'), 1.0, 3, 'taxes',\n",
" 'boring data'],\n",
" [1.0, Timestamp('2016-08-25 00:00:00'), 1.0, 3, 'tools',\n",
" 'boring data']], dtype=object)"
]
},
"execution_count": 14,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df_from_dictionary.values"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### display only the index(values) of dataframe object"
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"DatetimeIndex(['2016-01-01', '2016-01-02', '2016-01-03', '2016-01-04',\n",
" '2016-01-05', '2016-01-06'],\n",
" dtype='datetime64[ns]', freq='D')"
]
},
"execution_count": 15,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"sample_df.index"
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"Int64Index([0, 1, 2, 3], dtype='int64')"
]
},
"execution_count": 16,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df_from_dictionary.index"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### display only the columns(values) of dataframe object"
]
},
{
"cell_type": "code",
"execution_count": 17,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"Index(['A', 'B', 'C', 'D'], dtype='object')"
]
},
"execution_count": 17,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"sample_df.columns"
]
},
{
"cell_type": "code",
"execution_count": 18,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"Index(['float', 'time', 'series', 'array', 'categories', 'dull'], dtype='object')"
]
},
"execution_count": 18,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df_from_dictionary.columns"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### describe a dataframe object\n",
"##### describe(): a quick statistical summary\n",
"- notice: integer data summarized with floating point numbers"
]
},
{
"cell_type": "code",
"execution_count": 19,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>A</th>\n",
" <th>B</th>\n",
" <th>C</th>\n",
" <th>D</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>count</th>\n",
" <td>6.000000</td>\n",
" <td>6.000000</td>\n",
" <td>6.000000</td>\n",
" <td>6.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>mean</th>\n",
" <td>10.000000</td>\n",
" <td>11.000000</td>\n",
" <td>12.000000</td>\n",
" <td>13.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>std</th>\n",
" <td>7.483315</td>\n",
" <td>7.483315</td>\n",
" <td>7.483315</td>\n",
" <td>7.483315</td>\n",
" </tr>\n",
" <tr>\n",
" <th>min</th>\n",
" <td>0.000000</td>\n",
" <td>1.000000</td>\n",
" <td>2.000000</td>\n",
" <td>3.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>25%</th>\n",
" <td>5.000000</td>\n",
" <td>6.000000</td>\n",
" <td>7.000000</td>\n",
" <td>8.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>50%</th>\n",
" <td>10.000000</td>\n",
" <td>11.000000</td>\n",
" <td>12.000000</td>\n",
" <td>13.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>75%</th>\n",
" <td>15.000000</td>\n",
" <td>16.000000</td>\n",
" <td>17.000000</td>\n",
" <td>18.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>max</th>\n",
" <td>20.000000</td>\n",
" <td>21.000000</td>\n",
" <td>22.000000</td>\n",
" <td>23.000000</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" A B C D\n",
"count 6.000000 6.000000 6.000000 6.000000\n",
"mean 10.000000 11.000000 12.000000 13.000000\n",
"std 7.483315 7.483315 7.483315 7.483315\n",
"min 0.000000 1.000000 2.000000 3.000000\n",
"25% 5.000000 6.000000 7.000000 8.000000\n",
"50% 10.000000 11.000000 12.000000 13.000000\n",
"75% 15.000000 16.000000 17.000000 18.000000\n",
"max 20.000000 21.000000 22.000000 23.000000"
]
},
"execution_count": 19,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"sample_df.describe()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### control precision of floating point numbers in dataframe object\n",
"**remember that we need set this option on root level that is we need set the property to main pandas object**"
]
},
{
"cell_type": "code",
"execution_count": 20,
"metadata": {},
"outputs": [],
"source": [
"pd.set_option('display.precision', 2)"
]
},
{
"cell_type": "code",
"execution_count": 21,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>A</th>\n",
" <th>B</th>\n",
" <th>C</th>\n",
" <th>D</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>count</th>\n",
" <td>6.00</td>\n",
" <td>6.00</td>\n",
" <td>6.00</td>\n",
" <td>6.00</td>\n",
" </tr>\n",
" <tr>\n",
" <th>mean</th>\n",
" <td>10.00</td>\n",
" <td>11.00</td>\n",
" <td>12.00</td>\n",
" <td>13.00</td>\n",
" </tr>\n",
" <tr>\n",
" <th>std</th>\n",
" <td>7.48</td>\n",
" <td>7.48</td>\n",
" <td>7.48</td>\n",
" <td>7.48</td>\n",
" </tr>\n",
" <tr>\n",
" <th>min</th>\n",
" <td>0.00</td>\n",
" <td>1.00</td>\n",
" <td>2.00</td>\n",
" <td>3.00</td>\n",
" </tr>\n",
" <tr>\n",
" <th>25%</th>\n",
" <td>5.00</td>\n",
" <td>6.00</td>\n",
" <td>7.00</td>\n",
" <td>8.00</td>\n",
" </tr>\n",
" <tr>\n",
" <th>50%</th>\n",
" <td>10.00</td>\n",
" <td>11.00</td>\n",
" <td>12.00</td>\n",
" <td>13.00</td>\n",
" </tr>\n",
" <tr>\n",
" <th>75%</th>\n",
" <td>15.00</td>\n",
" <td>16.00</td>\n",
" <td>17.00</td>\n",
" <td>18.00</td>\n",
" </tr>\n",
" <tr>\n",
" <th>max</th>\n",
" <td>20.00</td>\n",
" <td>21.00</td>\n",
" <td>22.00</td>\n",
" <td>23.00</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" A B C D\n",
"count 6.00 6.00 6.00 6.00\n",
"mean 10.00 11.00 12.00 13.00\n",
"std 7.48 7.48 7.48 7.48\n",
"min 0.00 1.00 2.00 3.00\n",
"25% 5.00 6.00 7.00 8.00\n",
"50% 10.00 11.00 12.00 13.00\n",
"75% 15.00 16.00 17.00 18.00\n",
"max 20.00 21.00 22.00 23.00"
]
},
"execution_count": 21,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"sample_df.describe() # comparing the above code we can see the difference in decimal values for the same dataframe object"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### sort data by axis (by column)"
]
},
{
"cell_type": "code",
"execution_count": 22,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>D</th>\n",
" <th>C</th>\n",
" <th>B</th>\n",
" <th>A</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>2016-01-01</th>\n",
" <td>3</td>\n",
" <td>2</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2016-01-02</th>\n",
" <td>7</td>\n",
" <td>6</td>\n",
" <td>5</td>\n",
" <td>4</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2016-01-03</th>\n",
" <td>11</td>\n",
" <td>10</td>\n",
" <td>9</td>\n",
" <td>8</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2016-01-04</th>\n",
" <td>15</td>\n",
" <td>14</td>\n",
" <td>13</td>\n",
" <td>12</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2016-01-05</th>\n",
" <td>19</td>\n",
" <td>18</td>\n",
" <td>17</td>\n",
" <td>16</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2016-01-06</th>\n",
" <td>23</td>\n",
" <td>22</td>\n",
" <td>21</td>\n",
" <td>20</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" D C B A\n",
"2016-01-01 3 2 1 0\n",
"2016-01-02 7 6 5 4\n",
"2016-01-03 11 10 9 8\n",
"2016-01-04 15 14 13 12\n",
"2016-01-05 19 18 17 16\n",
"2016-01-06 23 22 21 20"
]
},
"execution_count": 22,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"sample_df.sort_index(axis=1, ascending=False) # axis = 1 means that we want sort the dataframe by column, sort from D to A"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### sort data by axis (by row)"
]
},
{
"cell_type": "code",
"execution_count": 23,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
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" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
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"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>A</th>\n",
" <th>B</th>\n",
" <th>C</th>\n",
" <th>D</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>2016-01-06</th>\n",
" <td>20</td>\n",
" <td>21</td>\n",
" <td>22</td>\n",
" <td>23</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2016-01-05</th>\n",
" <td>16</td>\n",
" <td>17</td>\n",
" <td>18</td>\n",
" <td>19</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2016-01-04</th>\n",
" <td>12</td>\n",
" <td>13</td>\n",
" <td>14</td>\n",
" <td>15</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2016-01-03</th>\n",
" <td>8</td>\n",
" <td>9</td>\n",
" <td>10</td>\n",
" <td>11</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2016-01-02</th>\n",
" <td>4</td>\n",
" <td>5</td>\n",
" <td>6</td>\n",
" <td>7</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2016-01-01</th>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>2</td>\n",
" <td>3</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" A B C D\n",
"2016-01-06 20 21 22 23\n",
"2016-01-05 16 17 18 19\n",
"2016-01-04 12 13 14 15\n",
"2016-01-03 8 9 10 11\n",
"2016-01-02 4 5 6 7\n",
"2016-01-01 0 1 2 3"
]
},
"execution_count": 23,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"sample_df.sort_index(axis=0, ascending=False) # axis = 0 means that we want sort the dataframe by row"
]
},
{
"cell_type": "code",
"execution_count": 24,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
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" <th></th>\n",
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" <th>C</th>\n",
" <th>D</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>1</td>\n",
" <td>2</td>\n",
" <td>6</td>\n",
" <td>4</td>\n",
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" <tr>\n",
" <th>1</th>\n",
" <td>2</td>\n",
" <td>4</td>\n",
" <td>9</td>\n",
" <td>3</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>7</td>\n",
" <td>8</td>\n",
" <td>4</td>\n",
" <td>6</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>4</td>\n",
" <td>5</td>\n",
" <td>1</td>\n",
" <td>8</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" A B C D\n",
"0 1 2 6 4\n",
"1 2 4 9 3\n",
"2 7 8 4 6\n",
"3 4 5 1 8"
]
},
"execution_count": 24,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"another_df_from_dictionary = pd.DataFrame({ \n",
" 'A' : np.array([1,2,7,4]),\n",
" 'B' : np.array([2,4,8,5]),\n",
" 'C' : np.array([6,9,4,1]),\n",
" 'D' : np.array([4,3,6,8]),\n",
" })\n",
"another_df_from_dictionary"
]
},
{
"cell_type": "code",
"execution_count": 25,
"metadata": {
"scrolled": true
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
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" <th></th>\n",
" <th>D</th>\n",
" <th>C</th>\n",
" <th>B</th>\n",
" <th>A</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>4</td>\n",
" <td>6</td>\n",
" <td>2</td>\n",
" <td>1</td>\n",
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" <tr>\n",
" <th>1</th>\n",
" <td>3</td>\n",
" <td>9</td>\n",
" <td>4</td>\n",
" <td>2</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>6</td>\n",
" <td>4</td>\n",
" <td>8</td>\n",
" <td>7</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>8</td>\n",
" <td>1</td>\n",
" <td>5</td>\n",
" <td>4</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" D C B A\n",
"0 4 6 2 1\n",
"1 3 9 4 2\n",
"2 6 4 8 7\n",
"3 8 1 5 4"
]
},
"execution_count": 25,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"another_df_from_dictionary.sort_index(axis=1, ascending=False)"
]
},
{
"cell_type": "code",
"execution_count": 26,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
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" <th>C</th>\n",
" <th>D</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>4</td>\n",
" <td>5</td>\n",
" <td>1</td>\n",
" <td>8</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>7</td>\n",
" <td>8</td>\n",
" <td>4</td>\n",
" <td>6</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>2</td>\n",
" <td>4</td>\n",
" <td>9</td>\n",
" <td>3</td>\n",
" </tr>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>1</td>\n",
" <td>2</td>\n",
" <td>6</td>\n",
" <td>4</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" A B C D\n",
"3 4 5 1 8\n",
"2 7 8 4 6\n",
"1 2 4 9 3\n",
"0 1 2 6 4"
]
},
"execution_count": 26,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"another_df_from_dictionary.sort_index(axis=0, ascending=False) # sort from 3 to 0"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### sort by data within a column"
]
},
{
"cell_type": "code",
"execution_count": 27,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>A</th>\n",
" <th>B</th>\n",
" <th>C</th>\n",
" <th>D</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>2016-01-06</th>\n",
" <td>20</td>\n",
" <td>21</td>\n",
" <td>22</td>\n",
" <td>23</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2016-01-05</th>\n",
" <td>16</td>\n",
" <td>17</td>\n",
" <td>18</td>\n",
" <td>19</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2016-01-04</th>\n",
" <td>12</td>\n",
" <td>13</td>\n",
" <td>14</td>\n",
" <td>15</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2016-01-03</th>\n",
" <td>8</td>\n",
" <td>9</td>\n",
" <td>10</td>\n",
" <td>11</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2016-01-02</th>\n",
" <td>4</td>\n",
" <td>5</td>\n",
" <td>6</td>\n",
" <td>7</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2016-01-01</th>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>2</td>\n",
" <td>3</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" A B C D\n",
"2016-01-06 20 21 22 23\n",
"2016-01-05 16 17 18 19\n",
"2016-01-04 12 13 14 15\n",
"2016-01-03 8 9 10 11\n",
"2016-01-02 4 5 6 7\n",
"2016-01-01 0 1 2 3"
]
},
"execution_count": 27,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"sample_df.sort_values(by='B', ascending=False)"
]
},
{
"cell_type": "code",
"execution_count": 28,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>A</th>\n",
" <th>B</th>\n",
" <th>C</th>\n",
" <th>D</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>2</td>\n",
" <td>4</td>\n",
" <td>9</td>\n",
" <td>3</td>\n",
" </tr>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>1</td>\n",
" <td>2</td>\n",
" <td>6</td>\n",
" <td>4</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>7</td>\n",
" <td>8</td>\n",
" <td>4</td>\n",
" <td>6</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>4</td>\n",
" <td>5</td>\n",
" <td>1</td>\n",
" <td>8</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" A B C D\n",
"1 2 4 9 3\n",
"0 1 2 6 4\n",
"2 7 8 4 6\n",
"3 4 5 1 8"
]
},
"execution_count": 28,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"another_df_from_dictionary.sort_values(by=\"C\", ascending=False)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### transpose rows and columns"
]
},
{
"cell_type": "code",
"execution_count": 29,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>2016-01-01 00:00:00</th>\n",
" <th>2016-01-02 00:00:00</th>\n",
" <th>2016-01-03 00:00:00</th>\n",
" <th>2016-01-04 00:00:00</th>\n",
" <th>2016-01-05 00:00:00</th>\n",
" <th>2016-01-06 00:00:00</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>A</th>\n",
" <td>0</td>\n",
" <td>4</td>\n",
" <td>8</td>\n",
" <td>12</td>\n",
" <td>16</td>\n",
" <td>20</td>\n",
" </tr>\n",
" <tr>\n",
" <th>B</th>\n",
" <td>1</td>\n",
" <td>5</td>\n",
" <td>9</td>\n",
" <td>13</td>\n",
" <td>17</td>\n",
" <td>21</td>\n",
" </tr>\n",
" <tr>\n",
" <th>C</th>\n",
" <td>2</td>\n",
" <td>6</td>\n",
" <td>10</td>\n",
" <td>14</td>\n",
" <td>18</td>\n",
" <td>22</td>\n",
" </tr>\n",
" <tr>\n",
" <th>D</th>\n",
" <td>3</td>\n",
" <td>7</td>\n",
" <td>11</td>\n",
" <td>15</td>\n",
" <td>19</td>\n",
" <td>23</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" 2016-01-01 2016-01-02 2016-01-03 2016-01-04 2016-01-05 2016-01-06\n",
"A 0 4 8 12 16 20\n",
"B 1 5 9 13 17 21\n",
"C 2 6 10 14 18 22\n",
"D 3 7 11 15 19 23"
]
},
"execution_count": 29,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"sample_df.T"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"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": "3.6.2"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
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