Pandas dataframe with and without index

code segment 1

plot_cols = ['Traffic_Measure']

plot_features = df[plot_cols]

index_dt = pd.to_datetime(df.pop('Timestamp(UTC)'), format='%m/%d/%y %H:%M')

_ = plot_features.plot(subplots=True)

code segment 2

plot_cols = ['Traffic_Measure']

plot_features = df[plot_cols]

index_dt = pd.to_datetime(df.pop('Timestamp(UTC)'), format='%m/%d/%y %H:%M')

plot_features.index = index_dt

_ = plot_features.plot(subplots=True)

Understanding the Difference Between the Two Code Segments

Key Difference: Index Setting

The primary difference between the two code segments lies in how the timestamp column is handled and used as the index for the DataFrame.

Code Segment 1:

The timestamp column is extracted and stored in the index_dt variable.

The plot_features DataFrame remains without an index, which means it will use the default integer index for plotting.

The subplots=True argument in plot_features.plot() will create subplots for each column in plot_features, but since there's only one column (Traffic_Measure), it will create a single subplot.

Code Segment 2:

The timestamp column is extracted and stored in the index_dt variable.

The index_dt is then set as the index for the plot_features DataFrame.

The subplots=True argument in plot_features.plot() will have no effect as there's only one column. It will create a single plot with the timestamp as the x-axis.

Implications:

Code Segment 1: Produces a single plot without a proper time index. The x-axis will be the default integer index.

Code Segment 2: Produces a time series plot with the timestamp as the x-axis, correctly visualizing the data over time.

Therefore, Code Segment 2 is the correct way to plot time series data.

By setting the timestamp column as the index, you ensure that the plot is created with time as the x-axis, accurately representing the time series data.

Additional Notes:

You might want to adjust the figure size and plot parameters (e.g., labels, title) for better visualization.

For more complex time series analysis, consider using libraries like Seaborn or Plotly, which offer advanced plotting capabilities.

By understanding these differences, you can effectively visualize your time series data and gain valuable insights.