Saving plots created using pandas can be done in several ways.
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You can find more about how to format bar charts on the pandas documentation website. text ( 0, 42.1, "Coldest \n temp \n v" ) plot ( kind = "bar", title = "Helsinki-Vantaa temperatures", xlabel = "Date", ylabel = "Temperature ", figsize = ( 12, 6 ), ylim =, ) # Add plot text ax. loc # Create bar plot ax = oct1_afternoon. # Define time range oct1_afternoon = oct1_temps. Similarly, text placement on a bar plot is more difficult, and most easily done using the index value of the bar where the text should be placed. For the y-axis, we can still define its range using the ylim= parameter. pandas sees bar plot data as categorical, so the date range is more difficult to define for x-axis limits.
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Note that it is easiest to plot our selected time range for a bar plot by selecting the dates in our data series first, rather than adjusting the plot limits. Bar plots are one option, which can be used quite similarly to line plots with the addition of the kind=bar parameter. In addition to line plots, there are many other options for plotting in pandas. text ( warm_time, 43.0, "Warmest time of the evening ->" ) Bar plots in pandas #
plot ( style = "k:", title = "Evening temperatures on October 1, Helsinki-Vantaa", xlabel = "Date", ylabel = "Temperature ", figsize = ( 12, 6 ), xlim =, ylim =, ) # Display text on plot ax. to_datetime ( "201910012120" ) # Create the plot, including the axis limits ax = oct1_temps. # Solution # Define start, end, and cold times start_time = pd.
In this case we’ll focus on weather observation station data from the Helsinki-Vantaa airport. In the lesson this week we are using some of the same weather observation data from Finland downloaded from NOAA that we used in Lesson 6. This is an optional part of the lesson, but will allow you to see an example for further exploration of interactive plotting using Pandas-Bokeh. Toward the end of the lesson we will also briefly explore creating interactive plots using the Pandas-Bokeh plotting backend, which allows us to produce plots similar to those available in the Bokeh plotting library using plotting syntax similar to that used normally in pandas. In particular, we will use features from the the pyplot module in Matplotlib, which provides MATLAB-like plotting. Plotting in pandas provides a basic framework for visualizing our data, but as you’ll see we will sometimes need to also use features from Matplotlib to enhance our plots. Much like the case of pandas being built upon NumPy, plotting in pandas takes advantage of plotting features from the Matplotlib plotting library. Others can follow the lesson and fill in their student notebooks using Binder.Īs we’re now familiar with some of the features of pandas, we will wade into visualizing our data in Python using the built-in plotting options available directly in pandas. Finnish university students are encouraged to use the CSC Notebooks platform.
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