![]() ![]() We can look at the column for the Bronx with: When you have the answer, see the Programming Problem List.Įach column in the original spreadsheet is a column, or series. What do the series functions: min(), median(), std(), and.What happens if you add in x = "Year", y = "Bronx"?.What happens if you leave off the x = "Year"? Why?.Print("The average number living in the Queens is", pop.mean()) Similarly the average (mean) population for Queens can be computed: Print("The largest number living in the Bronx is", pop.max()) For example, if you would like to know the maximum value for the series "Bronx", you apply the max() function to that series: There are useful built-in statistics functions for the dataframes in pandas. The last line shows the figure you created in a separate graphics window.It displayed the data as a visual plot of years versus borough populations.It read in a CSV file, containing NYC population historical data.We also imported the pyplot library which pandas uses to create figures. Imported the pandas library that contains structures and functions for organizing and visualizing data.Which makes a graphical display of all of the data series in the variable pop with the series corresponding to the column "Year" as the x-axis. The last line of our first pandas program is: It is a dataframe, described in the reading above: Pop = pd.read_csv('nycHistPop.csv',skiprows=5)īefore going on, let's print out the variable pop. ![]() It has an option to skip rows which we will use here: The pandas function for reading in CSV files is read_csv(). Note that it has 5 extra lines at the top before the column names occur. Year,Manhattan,Brooklyn,Queens,Bronx,Staten Island,Total * All population figures are consistent with present-day boundaries.,įirst census after the consolidation of the five boroughs, This is a " comma separated values" file- which is a plain text file containing spreadsheet data, with commas separating the different columns (thus, the name). Next, save the NYC historical population data to the same directory as your program. The as pd allows us to use pandas commands without writing out pandas everytime- we just write pd. The plotting commands without having to write matplotlib.pyplot everytime, instead we just write plt. We used matplotlib in the Lab 3 and Lab 4 for plotting. First, start your file with an import statements for pyplot and pandas: Let's use this to visualize the change in New York City's population. In Pandas, the basic structure is a DataFrame If you are using your own machine, see the directions at the end of Lab 1 for installing packages for Python.) (Pandas is installed on all the lab machines. It incorporates most of the Python constructs and libraries that we have seen thus far. Pandas, Python Data Analysis Library, is an elegant, open-source package for extracting, manipulating, and analyzing data, especially those stored in 2D arrays (like spreadsheets). To make reading files easier, we will use the Pandas library that lets you read in structured data files very efficiently. ![]()
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