Python Tutorial: Plotting with pandas

Want to learn more? Take the full course at https://learn.datacamp.com/courses/pandas-foundations at your own pace. More than a video, you'll learn hands-on coding & quickly apply skills to your daily work. --- Data visualization is a primary tool in a working data scientist's toolbox; let's see how to do it with pandas. For convenience, we import pandas as pd and matplotlib dot pyplot as plt. We load the AAPL stock data into a DataFrame using read_csv. Notice the options parse_date=True and index_col='date' to force a datetime64 index. Again, we'll use these alot with time series shortly. Also observe entries in the volume column significantly in magnitude than other columns. Now, we assign close_arr by indexing aapl 'close' (yielding a Series) and applying the values method (yielding a NumPy array). Remember, the command plot can plot NumPy arrays or lists and the command show must be executed to make the plot visible. This is the resulting plot of stock close prices. Notice the horizontal axis of the plot corresponds to date indices of the array. We can actually plot pandas Series directly. We assign close_series from aapl as a Series and call plot with close_series as an argument. The result is a similar plot but a bit nicer. The plot function automatically uses the Series's datetime index labels along the horizontal axis. An even nicer alternative is to use the pandas Series plot method; that is, apply close_series dot plot. The result is as before but with even more formatting on the axis labels and the name of the axis (date) inferred from the Index name. In fact, pandas DataFrames have a plot method just like pandas Series. Calling aapl dot plot plots all of the columns of DataFrame aapl on the same axes. Pandas plots each numerical column against the index and uses the column labels in the legend. However, on this scale, we can't see all five line plots because one is so much larger than all the others. We can produce a similar plot u