{
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{
"cell_type": "markdown",
"id": "58041ec7-6bbf-4fa8-b76d-1f4af1a3cccd",
"metadata": {},
"source": [
"Importing pandas, a leading data manipulation python library"
]
},
{
"cell_type": "code",
"execution_count": 1,
"id": "7c04b7c9-fd43-4f0c-91e5-bfca643d0bb6",
"metadata": {},
"outputs": [],
"source": [
"import pandas as pd"
]
},
{
"cell_type": "markdown",
"id": "92d05092-b42f-4943-bc7d-eff9db6198eb",
"metadata": {},
"source": [
"Reading in the data set."
]
},
{
"cell_type": "code",
"execution_count": 2,
"id": "32b6b294-acf7-411a-aee6-affd819fbc04",
"metadata": {},
"outputs": [],
"source": [
"df = pd.read_csv('raw/data.csv', names=['station_id', 'bike_id', 'from', 'to'])"
]
},
{
"cell_type": "markdown",
"id": "3980c0e3-1d94-4ccd-b863-c63e11561963",
"metadata": {},
"source": [
"Observing the data set"
]
},
{
"cell_type": "code",
"execution_count": 3,
"id": "c35503ec-e37c-4397-8e0d-8968fa18c70a",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"pandas.core.frame.DataFrame"
]
},
"execution_count": 3,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"type(df)"
]
},
{
"cell_type": "code",
"execution_count": 4,
"id": "baaa8207-6042-43cf-8433-d371238eac86",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"1000"
]
},
"execution_count": 4,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"len(df)"
]
},
{
"cell_type": "code",
"execution_count": 5,
"id": "c812ac6a-47cc-4f61-8612-a8e704fcfb66",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"(1000, 4)"
]
},
"execution_count": 5,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df.shape"
]
},
{
"cell_type": "code",
"execution_count": 6,
"id": "8987a405-9b2b-43bf-a049-aed7c21880f1",
"metadata": {
"tags": []
},
"outputs": [
{
"data": {
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"<div>\n",
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" 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>station_id</th>\n",
" <th>bike_id</th>\n",
" <th>from</th>\n",
" <th>to</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>212</td>\n",
" <td>2294</td>\n",
" <td>2020-12-13T11:26:54Z</td>\n",
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],
"text/plain": [
" station_id bike_id from to\n",
"0 212 2294 2020-12-13T11:26:54Z 2020-12-17T16:13:54Z\n",
"1 418 3441 2020-08-25T11:37:11Z 2020-08-31T11:18:11Z\n",
"2 301 6467 2021-04-10T17:05:16Z 2021-04-11T15:00:16Z\n",
"3 560 9386 2021-04-28T12:10:24Z 2021-05-02T07:31:24Z\n",
"4 87 8755 2021-01-10T10:40:53Z 2021-01-11T20:58:53Z\n",
"5 651 1525 2020-08-08T06:44:20Z 2020-08-11T01:15:20Z\n",
"6 40 8238 2021-01-16T10:20:47Z 2021-01-16T14:06:47Z\n",
"7 97 2437 2021-06-21T15:52:08Z 2021-06-24T00:56:08Z\n",
"8 362 8428 2021-03-12T16:38:10Z 2021-03-17T05:07:10Z\n",
"9 234 7266 2021-01-02T19:17:57Z 2021-01-08T01:42:57Z"
]
},
"execution_count": 6,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df.head(10)"
]
},
{
"cell_type": "code",
"execution_count": 7,
"id": "b96093e2-356d-46e8-8c09-7b0ae547886f",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"<class 'pandas.core.frame.DataFrame'>\n",
"RangeIndex: 1000 entries, 0 to 999\n",
"Data columns (total 4 columns):\n",
" # Column Non-Null Count Dtype \n",
"--- ------ -------------- ----- \n",
" 0 station_id 1000 non-null int64 \n",
" 1 bike_id 1000 non-null int64 \n",
" 2 from 1000 non-null object\n",
" 3 to 1000 non-null object\n",
"dtypes: int64(2), object(2)\n",
"memory usage: 31.4+ KB\n"
]
}
],
"source": [
"df.info()"
]
},
{
"cell_type": "markdown",
"id": "715ff011-2d0c-473a-a672-f9b632e69ad8",
"metadata": {},
"source": [
"Using the datetime objects in Pandas DataFrame."
]
},
{
"cell_type": "code",
"execution_count": 8,
"id": "bd204a28-f525-4f4e-9ef8-ba7fa30158ec",
"metadata": {},
"outputs": [],
"source": [
"df['from'] = pd.to_datetime(df['from'])\n",
"df['to'] = pd.to_datetime(df['to'])"
]
},
{
"cell_type": "markdown",
"id": "932734b3-4155-4491-924e-57fcd8fc24d2",
"metadata": {},
"source": [
"Subracting the two date columns and return a Series of timedelta objects with the time diff between each row."
]
},
{
"cell_type": "code",
"execution_count": 9,
"id": "74b0da85-5b62-4b81-a8a9-6a3f4196539c",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"0 4 days 04:47:00\n",
"1 5 days 23:41:00\n",
"2 0 days 21:55:00\n",
"3 3 days 19:21:00\n",
"4 1 days 10:18:00\n",
" ... \n",
"995 5 days 02:16:00\n",
"996 0 days 20:08:00\n",
"997 5 days 14:35:00\n",
"998 3 days 04:50:00\n",
"999 3 days 00:12:00\n",
"Length: 1000, dtype: timedelta64[ns]"
]
},
"execution_count": 9,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"difference = (df['to'] - df['from'])\n",
"difference"
]
},
{
"cell_type": "code",
"execution_count": 10,
"id": "c6fc4dd7-cefd-4d1f-b26e-32865137521f",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"3 days 13:43:53.760000 is the average (mean) journey duration across all bikes and all stations for this reporting period.\n"
]
}
],
"source": [
"avg_journey_duration = difference.mean()\n",
"print(f'{avg_journey_duration} is the average (mean) journey duration across all bikes and all stations for this reporting period.')"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "6cb0be5f-6889-4d04-9704-9c4a7567ac66",
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
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"name": "python3"
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