{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Pandas 4: Working with date- and time-based data" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "
\"pandas
\n", "\n", "\n", "---" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Overview \n", "\n", "In this notebook, we'll work with Pandas `DataFrame` and `Series` objects to do the following:\n", "1. Work with Pandas' implementation of methods and attributes from Python's `datetime` library\n", "1. Relabel a Series from a column whose values are date and time strings\n", "1. Employ a `lambda` function to convert date/time strings to `datetime` objects\n", "1. Use Pandas' built-in `plot` function to generate a basic time series plot\n", "1. Improve the look of the time series plot by using Matplotlib\n", "\n", "We'll once again use NYS Mesonet data, but for the entire day of 2 September 2021." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Prerequisites\n", "\n", "| Concepts | Importance | Notes |\n", "| --- | --- | --- |\n", "| Matplotlib | Necessary | |\n", "| Datetime | Helpful | |\n", "| Pandas | Necessary | Notebooks 1-3 |\n", "\n", "* **Time to learn**: 30 minutes" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "___" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Imports" ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "import pandas as pd\n", "import matplotlib.pyplot as plt\n", "from datetime import datetime" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Create a `DataFrame` objects from a csv file that contains NYSM observational data. Choose the station ID as the row index." ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [], "source": [ "dataFile = '/spare11/atm533/data/nysm_data_20210902.csv'\n", "nysm_data = pd.read_csv(dataFile,index_col='station')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Examine the `nysm_data` object." ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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timetemp_2m [degC]temp_9m [degC]relative_humidity [percent]precip_incremental [mm]precip_local [mm]precip_max_intensity [mm/min]avg_wind_speed_prop [m/s]max_wind_speed_prop [m/s]wind_speed_stddev_prop [m/s]...snow_depth [cm]frozen_soil_05cm [bit]frozen_soil_25cm [bit]frozen_soil_50cm [bit]soil_temp_05cm [degC]soil_temp_25cm [degC]soil_temp_50cm [degC]soil_moisture_05cm [m^3/m^3]soil_moisture_25cm [m^3/m^3]soil_moisture_50cm [m^3/m^3]
station
ADDI2021-09-02 00:00:00 UTC14.814.893.10.09.560.03.06.01.1...NaN0.00.00.020.120.519.90.510.440.44
ADDI2021-09-02 00:05:00 UTC14.614.793.30.00.000.02.84.20.6...NaN0.00.00.020.120.519.90.510.440.44
ADDI2021-09-02 00:10:00 UTC14.614.693.60.00.000.03.04.90.9...NaN0.00.00.020.120.519.90.510.440.44
ADDI2021-09-02 00:15:00 UTC14.614.693.70.00.000.02.95.30.9...NaN0.00.00.020.120.519.90.510.440.44
ADDI2021-09-02 00:20:00 UTC14.514.593.90.00.000.02.54.50.7...NaN0.00.00.020.120.519.90.510.440.44
..................................................................
YORK2021-09-02 23:35:00 UTC15.916.369.80.00.210.02.24.00.6...NaN0.00.00.020.520.821.10.120.240.23
YORK2021-09-02 23:40:00 UTC15.916.270.20.00.210.02.23.40.5...NaN0.00.00.020.520.921.10.120.240.23
YORK2021-09-02 23:45:00 UTC15.616.071.60.00.210.01.63.10.4...NaN0.00.00.020.520.721.10.120.240.23
YORK2021-09-02 23:50:00 UTC15.516.072.10.00.210.02.22.90.4...NaN0.00.00.020.520.821.10.120.240.24
YORK2021-09-02 23:55:00 UTC15.115.773.70.00.210.01.52.30.4...NaN0.00.00.020.520.921.10.120.240.23
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36288 rows × 29 columns

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" ], "text/plain": [ " time temp_2m [degC] temp_9m [degC] \\\n", "station \n", "ADDI 2021-09-02 00:00:00 UTC 14.8 14.8 \n", "ADDI 2021-09-02 00:05:00 UTC 14.6 14.7 \n", "ADDI 2021-09-02 00:10:00 UTC 14.6 14.6 \n", "ADDI 2021-09-02 00:15:00 UTC 14.6 14.6 \n", "ADDI 2021-09-02 00:20:00 UTC 14.5 14.5 \n", "... ... ... ... \n", "YORK 2021-09-02 23:35:00 UTC 15.9 16.3 \n", "YORK 2021-09-02 23:40:00 UTC 15.9 16.2 \n", "YORK 2021-09-02 23:45:00 UTC 15.6 16.0 \n", "YORK 2021-09-02 23:50:00 UTC 15.5 16.0 \n", "YORK 2021-09-02 23:55:00 UTC 15.1 15.7 \n", "\n", " relative_humidity [percent] precip_incremental [mm] \\\n", "station \n", "ADDI 93.1 0.0 \n", "ADDI 93.3 0.0 \n", "ADDI 93.6 0.0 \n", "ADDI 93.7 0.0 \n", "ADDI 93.9 0.0 \n", "... ... ... \n", "YORK 69.8 0.0 \n", "YORK 70.2 0.0 \n", "YORK 71.6 0.0 \n", "YORK 72.1 0.0 \n", "YORK 73.7 0.0 \n", "\n", " precip_local [mm] precip_max_intensity [mm/min] \\\n", "station \n", "ADDI 9.56 0.0 \n", "ADDI 0.00 0.0 \n", "ADDI 0.00 0.0 \n", "ADDI 0.00 0.0 \n", "ADDI 0.00 0.0 \n", "... ... ... \n", "YORK 0.21 0.0 \n", "YORK 0.21 0.0 \n", "YORK 0.21 0.0 \n", "YORK 0.21 0.0 \n", "YORK 0.21 0.0 \n", "\n", " avg_wind_speed_prop [m/s] max_wind_speed_prop [m/s] \\\n", "station \n", "ADDI 3.0 6.0 \n", "ADDI 2.8 4.2 \n", "ADDI 3.0 4.9 \n", "ADDI 2.9 5.3 \n", "ADDI 2.5 4.5 \n", "... ... ... \n", "YORK 2.2 4.0 \n", "YORK 2.2 3.4 \n", "YORK 1.6 3.1 \n", "YORK 2.2 2.9 \n", "YORK 1.5 2.3 \n", "\n", " wind_speed_stddev_prop [m/s] ... snow_depth [cm] \\\n", "station ... \n", "ADDI 1.1 ... NaN \n", "ADDI 0.6 ... NaN \n", "ADDI 0.9 ... NaN \n", "ADDI 0.9 ... NaN \n", "ADDI 0.7 ... NaN \n", "... ... ... ... \n", "YORK 0.6 ... NaN \n", "YORK 0.5 ... NaN \n", "YORK 0.4 ... NaN \n", "YORK 0.4 ... NaN \n", "YORK 0.4 ... NaN \n", "\n", " frozen_soil_05cm [bit] frozen_soil_25cm [bit] \\\n", "station \n", "ADDI 0.0 0.0 \n", "ADDI 0.0 0.0 \n", "ADDI 0.0 0.0 \n", "ADDI 0.0 0.0 \n", "ADDI 0.0 0.0 \n", "... ... ... \n", "YORK 0.0 0.0 \n", "YORK 0.0 0.0 \n", "YORK 0.0 0.0 \n", "YORK 0.0 0.0 \n", "YORK 0.0 0.0 \n", "\n", " frozen_soil_50cm [bit] soil_temp_05cm [degC] soil_temp_25cm [degC] \\\n", "station \n", "ADDI 0.0 20.1 20.5 \n", "ADDI 0.0 20.1 20.5 \n", "ADDI 0.0 20.1 20.5 \n", "ADDI 0.0 20.1 20.5 \n", "ADDI 0.0 20.1 20.5 \n", "... ... ... ... \n", "YORK 0.0 20.5 20.8 \n", "YORK 0.0 20.5 20.9 \n", "YORK 0.0 20.5 20.7 \n", "YORK 0.0 20.5 20.8 \n", "YORK 0.0 20.5 20.9 \n", "\n", " soil_temp_50cm [degC] soil_moisture_05cm [m^3/m^3] \\\n", "station \n", "ADDI 19.9 0.51 \n", "ADDI 19.9 0.51 \n", "ADDI 19.9 0.51 \n", "ADDI 19.9 0.51 \n", "ADDI 19.9 0.51 \n", "... ... ... \n", "YORK 21.1 0.12 \n", "YORK 21.1 0.12 \n", "YORK 21.1 0.12 \n", "YORK 21.1 0.12 \n", "YORK 21.1 0.12 \n", "\n", " soil_moisture_25cm [m^3/m^3] soil_moisture_50cm [m^3/m^3] \n", "station \n", "ADDI 0.44 0.44 \n", "ADDI 0.44 0.44 \n", "ADDI 0.44 0.44 \n", "ADDI 0.44 0.44 \n", "ADDI 0.44 0.44 \n", "... ... ... \n", "YORK 0.24 0.23 \n", "YORK 0.24 0.23 \n", "YORK 0.24 0.23 \n", "YORK 0.24 0.24 \n", "YORK 0.24 0.23 \n", "\n", "[36288 rows x 29 columns]" ] }, "execution_count": 3, "metadata": {}, "output_type": "execute_result" } ], "source": [ "nysm_data" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Work with Pandas' implementation of methods and attributes from Python's `datetime` library" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "
\n", " Tip: For a background on the use of datetime in Python, please view the supplementary notebook from this week, 05_Pandas_Supplement_Datetime.ipynb
" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Relabel a Series from a column whose values are date and time strings" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### First, let's load 5-minute accumulated precipitation for the Manhattan site." ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "station\n", "MANH 0.53\n", "MANH 1.37\n", "MANH 4.37\n", "MANH 1.83\n", "MANH 2.00\n", " ... \n", "MANH 0.00\n", "MANH 0.00\n", "MANH 0.00\n", "MANH 0.00\n", "MANH 0.00\n", "Name: precip_incremental [mm], Length: 288, dtype: float64" ] }, "execution_count": 4, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Select the column and row of interest\n", "prcpMANH = nysm_data['precip_incremental [mm]'].loc['MANH']\n", "prcpMANH" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Next, let's inspect the column correpsonding to date and time from the DataFrame." ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "station\n", "ADDI 2021-09-02 00:00:00 UTC\n", "ADDI 2021-09-02 00:05:00 UTC\n", "ADDI 2021-09-02 00:10:00 UTC\n", "ADDI 2021-09-02 00:15:00 UTC\n", "ADDI 2021-09-02 00:20:00 UTC\n", " ... \n", "YORK 2021-09-02 23:35:00 UTC\n", "YORK 2021-09-02 23:40:00 UTC\n", "YORK 2021-09-02 23:45:00 UTC\n", "YORK 2021-09-02 23:50:00 UTC\n", "YORK 2021-09-02 23:55:00 UTC\n", "Name: time, Length: 36288, dtype: object" ] }, "execution_count": 5, "metadata": {}, "output_type": "execute_result" } ], "source": [ "timeSer = nysm_data['time']\n", "timeSer" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### The *dtype: object* signifies that the values for *time* are being treated as a *string*. When working with time-based arrays, we want to treat them differently than a generic string type ... instead, let's treat them as `datetime` objects (derived from NumPy: see reference at end of notebook)." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "First, let's look at the output after converting the `Series` from string to `datetime`. To do that, we'll use the `to_datetime` method in Pandas. We pass in the Series, which consists of an array of strings, and then specify how the strings are *formatted*. See the reference at the end of the notebook for a guide to formatting date/time strings." ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "station\n", "ADDI 2021-09-02 00:00:00+00:00\n", "ADDI 2021-09-02 00:05:00+00:00\n", "ADDI 2021-09-02 00:10:00+00:00\n", "ADDI 2021-09-02 00:15:00+00:00\n", "ADDI 2021-09-02 00:20:00+00:00\n", " ... \n", "YORK 2021-09-02 23:35:00+00:00\n", "YORK 2021-09-02 23:40:00+00:00\n", "YORK 2021-09-02 23:45:00+00:00\n", "YORK 2021-09-02 23:50:00+00:00\n", "YORK 2021-09-02 23:55:00+00:00\n", "Name: time, Length: 36288, dtype: datetime64[ns, UTC]" ] }, "execution_count": 6, "metadata": {}, "output_type": "execute_result" } ], "source": [ "pd.to_datetime(timeSer, format = \"%Y-%m-%d %H:%M:%S UTC\", utc=True)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Notice that the `dtype` of the Series has changed to `datetime64`, with precision to the nanosecond level and a timezone of UTC." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "With the use of a `lambda` function, we can accomplish the string-->datetime conversion directly in the call to `read_csv`. We'll also now set the row index to be time." ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [], "source": [ "# First define the format and then define the function\n", "format = \"%Y-%m-%d %H:%M:%S UTC\"\n", "# This function will iterate over each string in a 1-d array \n", "# and use Pandas' implementation of strptime to convert the string into a datetime object.\n", "parseTime = lambda x: datetime.strptime(x, format)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Remind ourselves of how Pandas' `read_csv` method works:" ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "\u001b[0;31mSignature:\u001b[0m\n", "\u001b[0mpd\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mread_csv\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\u001b[0m\n", "\u001b[0;34m\u001b[0m \u001b[0mfilepath_or_buffer\u001b[0m\u001b[0;34m:\u001b[0m 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\u001b[0mat\u001b[0m \u001b[0;36m0x7f45202cb230\u001b[0m\u001b[0;34m>\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n", "\u001b[0;34m\u001b[0m \u001b[0mdelimiter\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n", "\u001b[0;34m\u001b[0m \u001b[0mheader\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'infer'\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n", "\u001b[0;34m\u001b[0m \u001b[0mnames\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n", "\u001b[0;34m\u001b[0m \u001b[0mindex_col\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n", "\u001b[0;34m\u001b[0m \u001b[0musecols\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n", "\u001b[0;34m\u001b[0m \u001b[0msqueeze\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mFalse\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n", "\u001b[0;34m\u001b[0m \u001b[0mprefix\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n", "\u001b[0;34m\u001b[0m \u001b[0mmangle_dupe_cols\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n", "\u001b[0;34m\u001b[0m \u001b[0mdtype\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n", "\u001b[0;34m\u001b[0m \u001b[0mengine\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n", "\u001b[0;34m\u001b[0m \u001b[0mconverters\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n", "\u001b[0;34m\u001b[0m \u001b[0mtrue_values\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n", "\u001b[0;34m\u001b[0m \u001b[0mfalse_values\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n", "\u001b[0;34m\u001b[0m \u001b[0mskipinitialspace\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mFalse\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n", "\u001b[0;34m\u001b[0m \u001b[0mskiprows\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n", "\u001b[0;34m\u001b[0m \u001b[0mskipfooter\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n", "\u001b[0;34m\u001b[0m \u001b[0mnrows\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n", "\u001b[0;34m\u001b[0m \u001b[0mna_values\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n", "\u001b[0;34m\u001b[0m \u001b[0mkeep_default_na\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n", "\u001b[0;34m\u001b[0m \u001b[0mna_filter\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n", "\u001b[0;34m\u001b[0m \u001b[0mverbose\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mFalse\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n", "\u001b[0;34m\u001b[0m \u001b[0mskip_blank_lines\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n", "\u001b[0;34m\u001b[0m \u001b[0mparse_dates\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mFalse\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n", "\u001b[0;34m\u001b[0m \u001b[0minfer_datetime_format\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mFalse\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n", "\u001b[0;34m\u001b[0m \u001b[0mkeep_date_col\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mFalse\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n", "\u001b[0;34m\u001b[0m \u001b[0mdate_parser\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n", "\u001b[0;34m\u001b[0m \u001b[0mdayfirst\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mFalse\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n", "\u001b[0;34m\u001b[0m \u001b[0mcache_dates\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n", "\u001b[0;34m\u001b[0m \u001b[0miterator\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mFalse\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n", "\u001b[0;34m\u001b[0m \u001b[0mchunksize\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n", "\u001b[0;34m\u001b[0m \u001b[0mcompression\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'infer'\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n", "\u001b[0;34m\u001b[0m \u001b[0mthousands\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n", "\u001b[0;34m\u001b[0m \u001b[0mdecimal\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mstr\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m'.'\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n", "\u001b[0;34m\u001b[0m \u001b[0mlineterminator\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n", "\u001b[0;34m\u001b[0m \u001b[0mquotechar\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'\"'\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n", "\u001b[0;34m\u001b[0m \u001b[0mquoting\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n", "\u001b[0;34m\u001b[0m \u001b[0mdoublequote\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n", "\u001b[0;34m\u001b[0m \u001b[0mescapechar\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n", "\u001b[0;34m\u001b[0m \u001b[0mcomment\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n", "\u001b[0;34m\u001b[0m \u001b[0mencoding\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n", "\u001b[0;34m\u001b[0m \u001b[0mdialect\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n", "\u001b[0;34m\u001b[0m \u001b[0merror_bad_lines\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n", "\u001b[0;34m\u001b[0m \u001b[0mwarn_bad_lines\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n", "\u001b[0;34m\u001b[0m \u001b[0mdelim_whitespace\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mFalse\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n", "\u001b[0;34m\u001b[0m \u001b[0mlow_memory\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n", "\u001b[0;34m\u001b[0m \u001b[0mmemory_map\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mFalse\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n", "\u001b[0;34m\u001b[0m \u001b[0mfloat_precision\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n", "\u001b[0;34m\u001b[0m \u001b[0mstorage_options\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mUnion\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mDict\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mstr\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mAny\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mNoneType\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n", "\u001b[0;34m\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", "\u001b[0;31mDocstring:\u001b[0m\n", "Read a comma-separated values (csv) file into DataFrame.\n", "\n", "Also supports optionally iterating or breaking of the file\n", "into chunks.\n", "\n", "Additional help can be found in the online docs for\n", "`IO Tools `_.\n", "\n", "Parameters\n", "----------\n", "filepath_or_buffer : str, path object or file-like object\n", " Any valid string path is acceptable. The string could be a URL. Valid\n", " URL schemes include http, ftp, s3, gs, and file. For file URLs, a host is\n", " expected. A local file could be: file://localhost/path/to/table.csv.\n", "\n", " If you want to pass in a path object, pandas accepts any ``os.PathLike``.\n", "\n", " By file-like object, we refer to objects with a ``read()`` method, such as\n", " a file handle (e.g. via builtin ``open`` function) or ``StringIO``.\n", "sep : str, default ','\n", " Delimiter to use. If sep is None, the C engine cannot automatically detect\n", " the separator, but the Python parsing engine can, meaning the latter will\n", " be used and automatically detect the separator by Python's builtin sniffer\n", " tool, ``csv.Sniffer``. In addition, separators longer than 1 character and\n", " different from ``'\\s+'`` will be interpreted as regular expressions and\n", " will also force the use of the Python parsing engine. Note that regex\n", " delimiters are prone to ignoring quoted data. Regex example: ``'\\r\\t'``.\n", "delimiter : str, default ``None``\n", " Alias for sep.\n", "header : int, list of int, default 'infer'\n", " Row number(s) to use as the column names, and the start of the\n", " data. Default behavior is to infer the column names: if no names\n", " are passed the behavior is identical to ``header=0`` and column\n", " names are inferred from the first line of the file, if column\n", " names are passed explicitly then the behavior is identical to\n", " ``header=None``. Explicitly pass ``header=0`` to be able to\n", " replace existing names. The header can be a list of integers that\n", " specify row locations for a multi-index on the columns\n", " e.g. [0,1,3]. Intervening rows that are not specified will be\n", " skipped (e.g. 2 in this example is skipped). Note that this\n", " parameter ignores commented lines and empty lines if\n", " ``skip_blank_lines=True``, so ``header=0`` denotes the first line of\n", " data rather than the first line of the file.\n", "names : array-like, optional\n", " List of column names to use. If the file contains a header row,\n", " then you should explicitly pass ``header=0`` to override the column names.\n", " Duplicates in this list are not allowed.\n", "index_col : int, str, sequence of int / str, or False, default ``None``\n", " Column(s) to use as the row labels of the ``DataFrame``, either given as\n", " string name or column index. If a sequence of int / str is given, a\n", " MultiIndex is used.\n", "\n", " Note: ``index_col=False`` can be used to force pandas to *not* use the first\n", " column as the index, e.g. when you have a malformed file with delimiters at\n", " the end of each line.\n", "usecols : list-like or callable, optional\n", " Return a subset of the columns. If list-like, all elements must either\n", " be positional (i.e. integer indices into the document columns) or strings\n", " that correspond to column names provided either by the user in `names` or\n", " inferred from the document header row(s). For example, a valid list-like\n", " `usecols` parameter would be ``[0, 1, 2]`` or ``['foo', 'bar', 'baz']``.\n", " Element order is ignored, so ``usecols=[0, 1]`` is the same as ``[1, 0]``.\n", " To instantiate a DataFrame from ``data`` with element order preserved use\n", " ``pd.read_csv(data, usecols=['foo', 'bar'])[['foo', 'bar']]`` for columns\n", " in ``['foo', 'bar']`` order or\n", " ``pd.read_csv(data, usecols=['foo', 'bar'])[['bar', 'foo']]``\n", " for ``['bar', 'foo']`` order.\n", "\n", " If callable, the callable function will be evaluated against the column\n", " names, returning names where the callable function evaluates to True. An\n", " example of a valid callable argument would be ``lambda x: x.upper() in\n", " ['AAA', 'BBB', 'DDD']``. Using this parameter results in much faster\n", " parsing time and lower memory usage.\n", "squeeze : bool, default False\n", " If the parsed data only contains one column then return a Series.\n", "prefix : str, optional\n", " Prefix to add to column numbers when no header, e.g. 'X' for X0, X1, ...\n", "mangle_dupe_cols : bool, default True\n", " Duplicate columns will be specified as 'X', 'X.1', ...'X.N', rather than\n", " 'X'...'X'. Passing in False will cause data to be overwritten if there\n", " are duplicate names in the columns.\n", "dtype : Type name or dict of column -> type, optional\n", " Data type for data or columns. E.g. {'a': np.float64, 'b': np.int32,\n", " 'c': 'Int64'}\n", " Use `str` or `object` together with suitable `na_values` settings\n", " to preserve and not interpret dtype.\n", " If converters are specified, they will be applied INSTEAD\n", " of dtype conversion.\n", "engine : {'c', 'python'}, optional\n", " Parser engine to use. The C engine is faster while the python engine is\n", " currently more feature-complete.\n", "converters : dict, optional\n", " Dict of functions for converting values in certain columns. Keys can either\n", " be integers or column labels.\n", "true_values : list, optional\n", " Values to consider as True.\n", "false_values : list, optional\n", " Values to consider as False.\n", "skipinitialspace : bool, default False\n", " Skip spaces after delimiter.\n", "skiprows : list-like, int or callable, optional\n", " Line numbers to skip (0-indexed) or number of lines to skip (int)\n", " at the start of the file.\n", "\n", " If callable, the callable function will be evaluated against the row\n", " indices, returning True if the row should be skipped and False otherwise.\n", " An example of a valid callable argument would be ``lambda x: x in [0, 2]``.\n", "skipfooter : int, default 0\n", " Number of lines at bottom of file to skip (Unsupported with engine='c').\n", "nrows : int, optional\n", " Number of rows of file to read. Useful for reading pieces of large files.\n", "na_values : scalar, str, list-like, or dict, optional\n", " Additional strings to recognize as NA/NaN. If dict passed, specific\n", " per-column NA values. By default the following values are interpreted as\n", " NaN: '', '#N/A', '#N/A N/A', '#NA', '-1.#IND', '-1.#QNAN', '-NaN', '-nan',\n", " '1.#IND', '1.#QNAN', '', 'N/A', 'NA', 'NULL', 'NaN', 'n/a',\n", " 'nan', 'null'.\n", "keep_default_na : bool, default True\n", " Whether or not to include the default NaN values when parsing the data.\n", " Depending on whether `na_values` is passed in, the behavior is as follows:\n", "\n", " * If `keep_default_na` is True, and `na_values` are specified, `na_values`\n", " is appended to the default NaN values used for parsing.\n", " * If `keep_default_na` is True, and `na_values` are not specified, only\n", " the default NaN values are used for parsing.\n", " * If `keep_default_na` is False, and `na_values` are specified, only\n", " the NaN values specified `na_values` are used for parsing.\n", " * If `keep_default_na` is False, and `na_values` are not specified, no\n", " strings will be parsed as NaN.\n", "\n", " Note that if `na_filter` is passed in as False, the `keep_default_na` and\n", " `na_values` parameters will be ignored.\n", "na_filter : bool, default True\n", " Detect missing value markers (empty strings and the value of na_values). In\n", " data without any NAs, passing na_filter=False can improve the performance\n", " of reading a large file.\n", "verbose : bool, default False\n", " Indicate number of NA values placed in non-numeric columns.\n", "skip_blank_lines : bool, default True\n", " If True, skip over blank lines rather than interpreting as NaN values.\n", "parse_dates : bool or list of int or names or list of lists or dict, default False\n", " The behavior is as follows:\n", "\n", " * boolean. If True -> try parsing the index.\n", " * list of int or names. e.g. If [1, 2, 3] -> try parsing columns 1, 2, 3\n", " each as a separate date column.\n", " * list of lists. e.g. If [[1, 3]] -> combine columns 1 and 3 and parse as\n", " a single date column.\n", " * dict, e.g. {'foo' : [1, 3]} -> parse columns 1, 3 as date and call\n", " result 'foo'\n", "\n", " If a column or index cannot be represented as an array of datetimes,\n", " say because of an unparsable value or a mixture of timezones, the column\n", " or index will be returned unaltered as an object data type. For\n", " non-standard datetime parsing, use ``pd.to_datetime`` after\n", " ``pd.read_csv``. To parse an index or column with a mixture of timezones,\n", " specify ``date_parser`` to be a partially-applied\n", " :func:`pandas.to_datetime` with ``utc=True``. See\n", " :ref:`io.csv.mixed_timezones` for more.\n", "\n", " Note: A fast-path exists for iso8601-formatted dates.\n", "infer_datetime_format : bool, default False\n", " If True and `parse_dates` is enabled, pandas will attempt to infer the\n", " format of the datetime strings in the columns, and if it can be inferred,\n", " switch to a faster method of parsing them. In some cases this can increase\n", " the parsing speed by 5-10x.\n", "keep_date_col : bool, default False\n", " If True and `parse_dates` specifies combining multiple columns then\n", " keep the original columns.\n", "date_parser : function, optional\n", " Function to use for converting a sequence of string columns to an array of\n", " datetime instances. The default uses ``dateutil.parser.parser`` to do the\n", " conversion. Pandas will try to call `date_parser` in three different ways,\n", " advancing to the next if an exception occurs: 1) Pass one or more arrays\n", " (as defined by `parse_dates`) as arguments; 2) concatenate (row-wise) the\n", " string values from the columns defined by `parse_dates` into a single array\n", " and pass that; and 3) call `date_parser` once for each row using one or\n", " more strings (corresponding to the columns defined by `parse_dates`) as\n", " arguments.\n", "dayfirst : bool, default False\n", " DD/MM format dates, international and European format.\n", "cache_dates : bool, default True\n", " If True, use a cache of unique, converted dates to apply the datetime\n", " conversion. May produce significant speed-up when parsing duplicate\n", " date strings, especially ones with timezone offsets.\n", "\n", " .. versionadded:: 0.25.0\n", "iterator : bool, default False\n", " Return TextFileReader object for iteration or getting chunks with\n", " ``get_chunk()``.\n", "\n", " .. versionchanged:: 1.2\n", "\n", " ``TextFileReader`` is a context manager.\n", "chunksize : int, optional\n", " Return TextFileReader object for iteration.\n", " See the `IO Tools docs\n", " `_\n", " for more information on ``iterator`` and ``chunksize``.\n", "\n", " .. versionchanged:: 1.2\n", "\n", " ``TextFileReader`` is a context manager.\n", "compression : {'infer', 'gzip', 'bz2', 'zip', 'xz', None}, default 'infer'\n", " For on-the-fly decompression of on-disk data. If 'infer' and\n", " `filepath_or_buffer` is path-like, then detect compression from the\n", " following extensions: '.gz', '.bz2', '.zip', or '.xz' (otherwise no\n", " decompression). If using 'zip', the ZIP file must contain only one data\n", " file to be read in. Set to None for no decompression.\n", "thousands : str, optional\n", " Thousands separator.\n", "decimal : str, default '.'\n", " Character to recognize as decimal point (e.g. use ',' for European data).\n", "lineterminator : str (length 1), optional\n", " Character to break file into lines. Only valid with C parser.\n", "quotechar : str (length 1), optional\n", " The character used to denote the start and end of a quoted item. Quoted\n", " items can include the delimiter and it will be ignored.\n", "quoting : int or csv.QUOTE_* instance, default 0\n", " Control field quoting behavior per ``csv.QUOTE_*`` constants. Use one of\n", " QUOTE_MINIMAL (0), QUOTE_ALL (1), QUOTE_NONNUMERIC (2) or QUOTE_NONE (3).\n", "doublequote : bool, default ``True``\n", " When quotechar is specified and quoting is not ``QUOTE_NONE``, indicate\n", " whether or not to interpret two consecutive quotechar elements INSIDE a\n", " field as a single ``quotechar`` element.\n", "escapechar : str (length 1), optional\n", " One-character string used to escape other characters.\n", "comment : str, optional\n", " Indicates remainder of line should not be parsed. If found at the beginning\n", " of a line, the line will be ignored altogether. This parameter must be a\n", " single character. Like empty lines (as long as ``skip_blank_lines=True``),\n", " fully commented lines are ignored by the parameter `header` but not by\n", " `skiprows`. For example, if ``comment='#'``, parsing\n", " ``#empty\\na,b,c\\n1,2,3`` with ``header=0`` will result in 'a,b,c' being\n", " treated as the header.\n", "encoding : str, optional\n", " Encoding to use for UTF when reading/writing (ex. 'utf-8'). `List of Python\n", " standard encodings\n", " `_ .\n", " .. versionchanged:: 1.2\n", "\n", " When ``encoding`` is ``None``, ``errors=\"replace\"`` is passed to\n", " ``open()``. Otherwise, ``errors=\"strict\"`` is passed to ``open()``.\n", " This behavior was previously only the case for ``engine=\"python\"``.\n", "dialect : str or csv.Dialect, optional\n", " If provided, this parameter will override values (default or not) for the\n", " following parameters: `delimiter`, `doublequote`, `escapechar`,\n", " `skipinitialspace`, `quotechar`, and `quoting`. If it is necessary to\n", " override values, a ParserWarning will be issued. See csv.Dialect\n", " documentation for more details.\n", "error_bad_lines : bool, default True\n", " Lines with too many fields (e.g. a csv line with too many commas) will by\n", " default cause an exception to be raised, and no DataFrame will be returned.\n", " If False, then these \"bad lines\" will dropped from the DataFrame that is\n", " returned.\n", "warn_bad_lines : bool, default True\n", " If error_bad_lines is False, and warn_bad_lines is True, a warning for each\n", " \"bad line\" will be output.\n", "delim_whitespace : bool, default False\n", " Specifies whether or not whitespace (e.g. ``' '`` or ``' '``) will be\n", " used as the sep. Equivalent to setting ``sep='\\s+'``. If this option\n", " is set to True, nothing should be passed in for the ``delimiter``\n", " parameter.\n", "low_memory : bool, default True\n", " Internally process the file in chunks, resulting in lower memory use\n", " while parsing, but possibly mixed type inference. To ensure no mixed\n", " types either set False, or specify the type with the `dtype` parameter.\n", " Note that the entire file is read into a single DataFrame regardless,\n", " use the `chunksize` or `iterator` parameter to return the data in chunks.\n", " (Only valid with C parser).\n", "memory_map : bool, default False\n", " If a filepath is provided for `filepath_or_buffer`, map the file object\n", " directly onto memory and access the data directly from there. Using this\n", " option can improve performance because there is no longer any I/O overhead.\n", "float_precision : str, optional\n", " Specifies which converter the C engine should use for floating-point\n", " values. The options are ``None`` or 'high' for the ordinary converter,\n", " 'legacy' for the original lower precision pandas converter, and\n", " 'round_trip' for the round-trip converter.\n", "\n", " .. versionchanged:: 1.2\n", "\n", "storage_options : dict, optional\n", " Extra options that make sense for a particular storage connection, e.g.\n", " host, port, username, password, etc., if using a URL that will\n", " be parsed by ``fsspec``, e.g., starting \"s3://\", \"gcs://\". An error\n", " will be raised if providing this argument with a non-fsspec URL.\n", " See the fsspec and backend storage implementation docs for the set of\n", " allowed keys and values.\n", "\n", " .. versionadded:: 1.2\n", "\n", "Returns\n", "-------\n", "DataFrame or TextParser\n", " A comma-separated values (csv) file is returned as two-dimensional\n", " data structure with labeled axes.\n", "\n", "See Also\n", "--------\n", "DataFrame.to_csv : Write DataFrame to a comma-separated values (csv) file.\n", "read_csv : Read a comma-separated values (csv) file into DataFrame.\n", "read_fwf : Read a table of fixed-width formatted lines into DataFrame.\n", "\n", "Examples\n", "--------\n", ">>> pd.read_csv('data.csv') # doctest: +SKIP\n", "\u001b[0;31mFile:\u001b[0m /knight/anaconda_jan21/envs/aug21/lib/python3.8/site-packages/pandas/io/parsers.py\n", "\u001b[0;31mType:\u001b[0m function\n" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "pd.read_csv?" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "___\n", "#### Re-create the *nysm_data* `DataFrame`, with appropriate additional arguments to `read_csv` (including our `lambda` function, via the `date_parser` argument)" ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [], "source": [ "nysm_data = pd.read_csv(dataFile,index_col=1,parse_dates=['time'], date_parser=parseTime)" ] }, { "cell_type": "code", "execution_count": 10, "metadata": { "scrolled": true }, "outputs": [ { "data": { "text/html": [ "
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stationtemp_2m [degC]temp_9m [degC]relative_humidity [percent]precip_incremental [mm]precip_local [mm]precip_max_intensity [mm/min]avg_wind_speed_prop [m/s]max_wind_speed_prop [m/s]wind_speed_stddev_prop [m/s]...snow_depth [cm]frozen_soil_05cm [bit]frozen_soil_25cm [bit]frozen_soil_50cm [bit]soil_temp_05cm [degC]soil_temp_25cm [degC]soil_temp_50cm [degC]soil_moisture_05cm [m^3/m^3]soil_moisture_25cm [m^3/m^3]soil_moisture_50cm [m^3/m^3]
time
2021-09-02 00:00:00ADDI14.814.893.10.09.560.03.06.01.1...NaN0.00.00.020.120.519.90.510.440.44
2021-09-02 00:05:00ADDI14.614.793.30.00.000.02.84.20.6...NaN0.00.00.020.120.519.90.510.440.44
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2 rows × 29 columns

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" ], "text/plain": [ " station temp_2m [degC] temp_9m [degC] \\\n", "time \n", "2021-09-02 00:00:00 ADDI 14.8 14.8 \n", "2021-09-02 00:05:00 ADDI 14.6 14.7 \n", "\n", " relative_humidity [percent] precip_incremental [mm] \\\n", "time \n", "2021-09-02 00:00:00 93.1 0.0 \n", "2021-09-02 00:05:00 93.3 0.0 \n", "\n", " precip_local [mm] precip_max_intensity [mm/min] \\\n", "time \n", "2021-09-02 00:00:00 9.56 0.0 \n", "2021-09-02 00:05:00 0.00 0.0 \n", "\n", " avg_wind_speed_prop [m/s] max_wind_speed_prop [m/s] \\\n", "time \n", "2021-09-02 00:00:00 3.0 6.0 \n", "2021-09-02 00:05:00 2.8 4.2 \n", "\n", " wind_speed_stddev_prop [m/s] ... snow_depth [cm] \\\n", "time ... \n", "2021-09-02 00:00:00 1.1 ... NaN \n", "2021-09-02 00:05:00 0.6 ... NaN \n", "\n", " frozen_soil_05cm [bit] frozen_soil_25cm [bit] \\\n", "time \n", "2021-09-02 00:00:00 0.0 0.0 \n", "2021-09-02 00:05:00 0.0 0.0 \n", "\n", " frozen_soil_50cm [bit] soil_temp_05cm [degC] \\\n", "time \n", "2021-09-02 00:00:00 0.0 20.1 \n", "2021-09-02 00:05:00 0.0 20.1 \n", "\n", " soil_temp_25cm [degC] soil_temp_50cm [degC] \\\n", "time \n", "2021-09-02 00:00:00 20.5 19.9 \n", "2021-09-02 00:05:00 20.5 19.9 \n", "\n", " soil_moisture_05cm [m^3/m^3] \\\n", "time \n", "2021-09-02 00:00:00 0.51 \n", "2021-09-02 00:05:00 0.51 \n", "\n", " soil_moisture_25cm [m^3/m^3] \\\n", "time \n", "2021-09-02 00:00:00 0.44 \n", "2021-09-02 00:05:00 0.44 \n", "\n", " soil_moisture_50cm [m^3/m^3] \n", "time \n", "2021-09-02 00:00:00 0.44 \n", "2021-09-02 00:05:00 0.44 \n", "\n", "[2 rows x 29 columns]" ] }, "execution_count": 10, "metadata": {}, "output_type": "execute_result" } ], "source": [ "nysm_data.head(2)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Now *time* is the `DataFrame`'s row index. Let's inspect this index; it's much like a generic Pandas `RangeIndex`, but specific for date/time purposes:" ] }, { "cell_type": "code", "execution_count": 11, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "DatetimeIndex(['2021-09-02 00:00:00', '2021-09-02 00:05:00',\n", " '2021-09-02 00:10:00', '2021-09-02 00:15:00',\n", " '2021-09-02 00:20:00', '2021-09-02 00:25:00',\n", " '2021-09-02 00:30:00', '2021-09-02 00:35:00',\n", " '2021-09-02 00:40:00', '2021-09-02 00:45:00',\n", " ...\n", " '2021-09-02 23:10:00', '2021-09-02 23:15:00',\n", " '2021-09-02 23:20:00', '2021-09-02 23:25:00',\n", " '2021-09-02 23:30:00', '2021-09-02 23:35:00',\n", " '2021-09-02 23:40:00', '2021-09-02 23:45:00',\n", " '2021-09-02 23:50:00', '2021-09-02 23:55:00'],\n", " dtype='datetime64[ns]', name='time', length=36288, freq=None)" ] }, "execution_count": 11, "metadata": {}, "output_type": "execute_result" } ], "source": [ "timeIndx = nysm_data.index\n", "timeIndx" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Note that the `timezone` is missing. The `read_csv` method does not provide a means to specify the timezone. We can take care of that though with the `tz_localize` method." ] }, { "cell_type": "code", "execution_count": 12, "metadata": {}, "outputs": [], "source": [ "timeIndx = timeIndx.tz_localize(tz='UTC')" ] }, { "cell_type": "code", "execution_count": 13, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "DatetimeIndex(['2021-09-02 00:00:00+00:00', '2021-09-02 00:05:00+00:00',\n", " '2021-09-02 00:10:00+00:00', '2021-09-02 00:15:00+00:00',\n", " '2021-09-02 00:20:00+00:00', '2021-09-02 00:25:00+00:00',\n", " '2021-09-02 00:30:00+00:00', '2021-09-02 00:35:00+00:00',\n", " '2021-09-02 00:40:00+00:00', '2021-09-02 00:45:00+00:00',\n", " ...\n", " '2021-09-02 23:10:00+00:00', '2021-09-02 23:15:00+00:00',\n", " '2021-09-02 23:20:00+00:00', '2021-09-02 23:25:00+00:00',\n", " '2021-09-02 23:30:00+00:00', '2021-09-02 23:35:00+00:00',\n", " '2021-09-02 23:40:00+00:00', '2021-09-02 23:45:00+00:00',\n", " '2021-09-02 23:50:00+00:00', '2021-09-02 23:55:00+00:00'],\n", " dtype='datetime64[ns, UTC]', name='time', length=36288, freq=None)" ] }, "execution_count": 13, "metadata": {}, "output_type": "execute_result" } ], "source": [ "timeIndx" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### If this were a `Series`, not an index, use this `Series`-specific method instead:\n", "`timeIndx= timeIndx.dt.tz_localize(tz='UTC')`" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Since it's a `datetime` object now, we can apply all sorts of time/date operations to it. For example, let's convert to Eastern time." ] }, { "cell_type": "code", "execution_count": 14, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "DatetimeIndex(['2021-09-01 20:00:00-04:00', '2021-09-01 20:05:00-04:00',\n", " '2021-09-01 20:10:00-04:00', '2021-09-01 20:15:00-04:00',\n", " '2021-09-01 20:20:00-04:00', '2021-09-01 20:25:00-04:00',\n", " '2021-09-01 20:30:00-04:00', '2021-09-01 20:35:00-04:00',\n", " '2021-09-01 20:40:00-04:00', '2021-09-01 20:45:00-04:00',\n", " ...\n", " '2021-09-02 19:10:00-04:00', '2021-09-02 19:15:00-04:00',\n", " '2021-09-02 19:20:00-04:00', '2021-09-02 19:25:00-04:00',\n", " '2021-09-02 19:30:00-04:00', '2021-09-02 19:35:00-04:00',\n", " '2021-09-02 19:40:00-04:00', '2021-09-02 19:45:00-04:00',\n", " '2021-09-02 19:50:00-04:00', '2021-09-02 19:55:00-04:00'],\n", " dtype='datetime64[ns, US/Eastern]', name='time', length=36288, freq=None)" ] }, "execution_count": 14, "metadata": {}, "output_type": "execute_result" } ], "source": [ "timeIndx = timeIndx.tz_convert(tz='US/Eastern')\n", "timeIndx" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### (Yes, it automatically accounts for Standard or Daylight time!)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Use Pandas' built-in `plot` function ... which leverages Matplotlib:\n", "#### Select all the rows for site MANH" ] }, { "cell_type": "code", "execution_count": 15, "metadata": {}, "outputs": [], "source": [ "condition = nysm_data['station'] == 'MANH'\n", "MANH = nysm_data.loc[condition]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Generate a basic time series plot by passing the desired column to Pandas' `plot` method:" ] }, { "cell_type": "code", "execution_count": 16, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 16, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "prcp = MANH['precip_incremental [mm]']\n", "prcp.plot()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### That was a way to get a quick look at the data and verify it looks reasonable. Now, let's pretty it up by using Matplotlib functions." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### We'll draw a line plot, passing in time and wind gust speed for the x- and y-axes, respectively. Follow the same procedure as we did in the Matplotlib notebooks from week 3." ] }, { "cell_type": "code", "execution_count": 17, "metadata": {}, "outputs": [ { "ename": "ValueError", "evalue": "x and y must have same first dimension, but have shapes (36288,) and (288,)", "output_type": "error", "traceback": [ "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", "\u001b[0;31mValueError\u001b[0m Traceback (most recent call last)", "\u001b[0;32m/tmp/ipykernel_12337/71833961.py\u001b[0m in \u001b[0;36m\u001b[0;34m\u001b[0m\n\u001b[1;32m 5\u001b[0m \u001b[0max\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mset_ylabel\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0;34m'5-min accum. precip (mm)'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 6\u001b[0m \u001b[0max\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mset_title\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0;34m\"Manhattan, NY 5-minute accumulated precip associated with the remnants of Hurricane Ida\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 7\u001b[0;31m \u001b[0max\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mplot\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mtimeIndx\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mprcp\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m", "\u001b[0;32m/knight/anaconda_jan21/envs/aug21/lib/python3.8/site-packages/matplotlib/axes/_axes.py\u001b[0m in \u001b[0;36mplot\u001b[0;34m(self, scalex, scaley, data, *args, **kwargs)\u001b[0m\n\u001b[1;32m 1603\u001b[0m \"\"\"\n\u001b[1;32m 1604\u001b[0m \u001b[0mkwargs\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcbook\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mnormalize_kwargs\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m,\u001b[0m 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\u001b[0my\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshape\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 501\u001b[0;31m raise ValueError(f\"x and y must have same first dimension, but \"\n\u001b[0m\u001b[1;32m 502\u001b[0m f\"have shapes {x.shape} and {y.shape}\")\n\u001b[1;32m 503\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mx\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mndim\u001b[0m \u001b[0;34m>\u001b[0m \u001b[0;36m2\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mndim\u001b[0m \u001b[0;34m>\u001b[0m \u001b[0;36m2\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", "\u001b[0;31mValueError\u001b[0m: x and y must have same first dimension, but have shapes (36288,) and (288,)" ] }, { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plt.style.use(\"seaborn\")\n", "fig = plt.figure(figsize=(11,8.5))\n", "ax = fig.add_subplot(1,1,1) \n", "ax.set_xlabel ('Date and Time')\n", "ax.set_ylabel ('5-min accum. precip (mm)')\n", "ax.set_title (\"Manhattan, NY 5-minute accumulated precip associated with the remnants of Hurricane Ida\")\n", "ax.plot (timeIndx, prcp)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Didn't work!!! Look at the error message above!\n", "#### This is a *mismatch* between array sizes. The time index is based on the entire Dataframe, which has 12 x 24 x 126 rows, while the Manhattan precip array is only 12 x 24!" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Let's set a condition where we match only those times that are in the same row as the Manhattan station id." ] }, { "cell_type": "code", "execution_count": 18, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "DatetimeIndex(['2021-09-01 20:00:00-04:00', '2021-09-01 20:05:00-04:00',\n", " '2021-09-01 20:10:00-04:00', '2021-09-01 20:15:00-04:00',\n", " '2021-09-01 20:20:00-04:00', '2021-09-01 20:25:00-04:00',\n", " '2021-09-01 20:30:00-04:00', '2021-09-01 20:35:00-04:00',\n", " '2021-09-01 20:40:00-04:00', '2021-09-01 20:45:00-04:00',\n", " ...\n", " '2021-09-02 19:10:00-04:00', '2021-09-02 19:15:00-04:00',\n", " '2021-09-02 19:20:00-04:00', '2021-09-02 19:25:00-04:00',\n", " '2021-09-02 19:30:00-04:00', '2021-09-02 19:35:00-04:00',\n", " '2021-09-02 19:40:00-04:00', '2021-09-02 19:45:00-04:00',\n", " '2021-09-02 19:50:00-04:00', '2021-09-02 19:55:00-04:00'],\n", " dtype='datetime64[ns, US/Eastern]', name='time', length=288, freq=None)" ] }, "execution_count": 18, "metadata": {}, "output_type": "execute_result" } ], "source": [ "condition = nysm_data['station'] == 'MANH'\n", "timeIndxMANH = timeIndx[condition]\n", "timeIndxMANH" ] }, { "cell_type": "code", "execution_count": 19, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plt.style.use(\"seaborn\")\n", "fig = plt.figure(figsize=(11,8.5))\n", "ax = fig.add_subplot(1,1,1) \n", "ax.set_xlabel ('Date and Time')\n", "ax.set_ylabel ('5-min accum. precip (mm)')\n", "ax.set_title (\"Manhattan, NY 5-minute accumulated precip associated with the remnants of Hurricane Ida\")\n", "ax.plot (timeIndxMANH, prcp);" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### That's looking better! We still have work to do to improve the labeling of the x-axis tick marks, but we'll save that for another time." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "
\n", " Explore further: Try making plots of other NYSM variables, from different NYSM sites.
" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "---\n", "## Summary\n", "\n", "* Use a `lambda` function to convert Date/time strings into Python `datetime` objects\n", "* Pandas' `plot` method allows for a quick visualization of `DataFrame` and `Series` objects. \n", "* x- and y- arrays must be of the same size in order to be plotted.\n", "\n", "### What's Next?\n", "Coming up next week, we will conclude our exploration of Pandas.\n", "\n", "## Resources and References\n", "1. [`datetime`objects in NumPy arrays](https://numpy.org/doc/stable/reference/arrays.datetime.html)\n", "1. [Date/time string formatting guide](https://strftime.org/)\n", "1. [Use of a `lambda` function in `read_csv` (Corey Schafer YouTube channel)](https://www.youtube.com/watch?v=UFuo7EHI8zc&list=PL-osiE80TeTsWmV9i9c58mdDCSskIFdDS&index=10&ab_channel=CoreySchafer)\n", "\n" ] } ], "metadata": { "kernelspec": { "display_name": "Python 3 August 2022 Environment", "language": "python", "name": "aug22" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.10.5" } }, "nbformat": 4, "nbformat_minor": 4 }