Seasonality Charts (Weekly data spanning over several years plotted such that each line corresponds to a year with x axis spanning over 52 weeks) - How to show months in axis instead of week numbers

📊 Plotly Python
1

I am getting data in a pandas dataframe, there are four columns in the dataframe ‘‘year’, week_number’, ‘week_ending_date’ and ‘value’. Data is for several years (2012 - 2019).

Each year is represented as a line in the chart using Scatter with x = df[‘week’] and y = df[‘value’].
So far so good. I am able to view the values against week number for each year.

Instead of showing the week number as x axis ticks, I would like to show the months (Jan, Feb, Mar etc.) I tried to use tickvals, ticktext approach however since more than one week falls in a month that didn’t work well.

I have pasted the code below. Data is read from a database in get_data_for_series call and it just returns a dataframe with four columns as described in the first paragraph above.

A screen shot of sample data is below

Data

One of the sub plots which is representative of others is below

Curve
Curve.PNG819×509 66.6 KB

def plot(series_names):

fig = make_subplots(
    rows=3,
    cols=2,
    horizontal_spacing=0.1,
    vertical_spacing=0.1,
    subplot_titles=series_names
)

showlegend = True

for col, series_name in enumerate(series_names):
    df_data = get_data_for_series(series_name)
    year_list = df_data['year'].unique().tolist()

    for idx, year in enumerate(year_list):
        df = df_data[df_data['year'] == year][['week_ending_date','week', 'value']].reset_index()

        df = df.sort_values(by=['week'])
        trace = go.Scatter(x=df['week'],
                       y=df['value'],
                       line=dict(width=PLOT_COLORS_WIDTH[year][1], color=PLOT_COLORS_WIDTH[year][0]),
                       mode='lines',
                       name=year,
                       legendgroup=str(year),
                       showlegend=showlegend)


        column_position = col % COL_COUNT
        row_position = math.ceil((col + 1) / COL_COUNT)

        fig.add_trace(trace, row=row_position, col=column_position + 1)

    fig.update_layout(title_text='',
                  title_font_size=12,
                  autosize=False,
                  plot_bgcolor=BACKGROUND_COLOR,
                  width=WIDTH,
                  height=HEIGHT)


    for i in range(1,7):
        fig['layout'][f'xaxis{i}'].update(title='Week Number',
                                       showgrid=True,
                                          mirror=True,
                                       linecolor='black',
                                       gridcolor=GRID_COLOR)

    for i in range(1,7):
        fig['layout'][f'yaxis{i}'].update(title='bbls',
                                       showgrid=True,
                                       mirror=True,
                                       linecolor='black',
                                       gridcolor=GRID_COLOR,
                                       tickformat='g')


    showlegend = False

#Set Sub Plot title Font
for annotation in fig['layout']['annotations']:
    annotation['font'] = dict(size=12)

print(fig['layout'])
fig.show()
2

I was able to fake the axis by changing the x values of the charts. Instead of using week numbers, I created a dataframe with dates of every week in a year (I chose 2016) and used that as x values. It now shows the months with formatting of xaxis changed to dtick=‘M1’

Charts now look like below.

GoodCurve
GoodCurve.PNG824×494 68.2 KB