Data Visualizations Pandas Plot Tutorial

Line Plot using Pandas

Import Modules

In [14]:
import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
% matplotlib inline

Generate a Line Plot from My Fitbit Activity Data

More often, you'll be asked to generate a line plot to show a trend over time.

Below is my Fitbit activity of steps for each day over a 15 day time period.

In [15]:
dates = ['2018-02-01', '2018-02-02', '2018-02-03', '2018-02-04',
         '2018-02-05', '2018-02-06', '2018-02-07', '2018-02-08',
         '2018-02-09', '2018-02-10', '2018-02-11', '2018-02-12',
         '2018-02-13', '2018-02-14', '2018-02-15']
In [16]:
steps = [11178, 9769, 11033, 9757, 10045, 9987, 11067, 11326, 9976,
                   11359, 10428, 10296, 9377, 10705, 9426]

Convert Data into Pandas Dataframe

We create a Pandas DataFrame from our lists, naming the columns date and steps.

In [17]:
df_fitbit_activity = pd.DataFrame(
    {'date': dates, 'steps': steps})
Preview top 5 rows of dataframe
In [18]:
date steps
0 2018-02-01 11178
1 2018-02-02 9769
2 2018-02-03 11033
3 2018-02-04 9757
4 2018-02-05 10045
See data types and count of values in fields
In [19]:
<class 'pandas.core.frame.DataFrame'>
RangeIndex: 15 entries, 0 to 14
Data columns (total 2 columns):
date     15 non-null object
steps    15 non-null int64
dtypes: int64(1), object(1)
memory usage: 320.0+ bytes

Convert Strings to Datetime Objects

In our plot, we want dates on the x-axis and steps on the y-axis.

However, Pandas plotting does not allow for strings - the data type in our dates list - to appear on the x-axis.

We must convert the dates as strings into datetime objects.

Use to_datetime method
In [20]:
df_fitbit_activity['date'] = pd.to_datetime(df_fitbit_activity['date'])
Verify date field changed to datetime type
In [21]:
<class 'pandas.core.frame.DataFrame'>
RangeIndex: 15 entries, 0 to 14
Data columns (total 2 columns):
date     15 non-null datetime64[ns]
steps    15 non-null int64
dtypes: datetime64[ns](1), int64(1)
memory usage: 320.0 bytes

The date field changed to have all values contain the datetime type.

Plot Steps Over Time

In a Pandas line plot, the index of the dataframe is plotted on the x-axis. Currently, we have an index of values from 0 to 15 on each integer increment.

In [22]:
RangeIndex(start=0, stop=15, step=1)

We need to set our date field to be the index of our dataframe so it's plotted accordingly on the x-axis.

Below, I utilize the Pandas Series plot method. Here is the official documentation page.

In [23]:

Style Plot

Below, I'll make lots of changes to our simple plot so it is easier to interpret.

Many of these steps are explained in more detail in my tutorial called Line Plots using Matplotlib.

In [31]:
df_fitbit_activity.set_index('date')['steps'].plot(figsize=(12, 10), linewidth=2.5, color='maroon')
plt.xlabel("Date", labelpad=15)
plt.ylabel("Daily Step Count", labelpad=15)
plt.title("My Daily Step Count Tracked by Fitbit", y=1.02, fontsize=22);