Plotting time series directly with Pandas

In the above dataframe, all I want to create a line plot so that we have info on trends per year for each of the columns. I’ve read about pivot-table on related posts, but when I implement that, it says there are no numbers to aggregate. I don’t want to aggregate something. I just need the y-axis in terms of the column numbers.

When I use plot() however, it plots year on the x-axis and only plots other column also on the x-axis. Why is this happening and what I am doing wrong?

Answer

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Quick Answer

# change the type of non numeric
piv['second_col'] = piv['second_col'].str.replace(',','').astype(float)
piv['last_col'] = piv['last_col'].str.replace(',','').astype(float)
# then plot
piv.plot(x='Year')

Explanation

The index of the Dataframe is the default x-axis, So you need to specify :

piv.plot(x='Year')

Or set the Year as index :

piv.set_index('Year').plot()

One more thing is that the plot function plot numeric values, the second and last columns type is string you can check :

df.dtypes

When you use pandas.read_csv to read a file it has to infer the data type. Sometimes it gets it wrong. You can forces pandas to try and convert the data to floating point numbers :

piv = piv.astype(float)

But you will get an error somthing like this :

ValueError: could not convert string to float: '2,499'

But Why ?

The data has a comma-separated numeric value, you need to remove it before converting to float

piv['name_of_column'] = piv['name_of_column'].str.replace(',','').astype(float)