Now, look closely at the dates on the x-axis. show () Line plot of precipitation in Boulder, CO with dates as strings and without no-data values removed.įirst, you will notice that there are many negative values in this dataset - these are actually “no data” values that you will handle in a later section on this page. set ( xlabel = "Date", ylabel = "Precipitation (inches)", title = "Daily Total Precipitation \n Boulder, Colorado in July 2018" ) plt. plot ( boulder_july_2018, boulder_july_2018, color = 'purple' ) # Set title and labels for axesĪx. subplots ( figsize = ( 10, 10 )) # Add x-axis and y-axisĪx. You will work with modules from pandas and matplotlib to plot dates more efficiently, and you will work with the seaborn package to make more attractive plots.įig, ax = plt. To begin, import the necessary packages to work with pandas dataframe and download data. On this page, you will learn how to handle dates using the datetime object in Python with pandas, using a dataset of daily temperature (maximum in Fahrenheit) and total precipitation (inches) in July 2018 for Boulder, CO, provided by the National Oceanic and Atmospheric Administration (NOAA). Set a “no data” value for a file when you import it into a pandas dataframe.ĭive Deeper Into Working With Datetime Objects in Python.Explain the role of “no data” values and how the NaN value is used in Python to label “no data” values.Use the datetime object to create easier-to-read time series plots and work with data across various timeframes (e.g.Import a time series dataset using pandas with dates converted to a datetime object in Python.Intermediate-earth-data-science-textbook HomeĪfter completing this chapter, you will be able to: Use Data for Earth and Environmental Science in Open Source Python Home.Chapter 12: Design and Automate Data Workflows.SECTION 7 INTRODUCTION TO API DATA ACCESS IN OPEN SOURCE PYTHON. SECTION 6 INTRODUCTION TO HIERARCHICAL DATA FORMATS IN PYTHON.Chapter 11: Calculate Vegetation Indices in Python.Chapter 7: Intro to Multispectral Remote Sensing Data.SECTION 5 MULTISPECTRAL REMOTE SENSING DATA IN PYTHON.Chapter 6: Uncertainty in Remote Sensing Data.SECTION 4 SPATIAL DATA APPLICATIONS IN PYTHON.Chapter 5: Processing Raster Data in Python.Chapter 4: Intro to Raster Data in Python.SECTION 3 INTRODUCTION TO RASTER DATA IN PYTHON.Chapter 3: Processing Spatial Vector Data in Python.SECTION 2 INTRO TO SPATIAL VECTOR DATA IN PYTHON.Chapter 1.5 Flood returns period analysis in python.
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