Negative values

Handling negative values

dayplot makes it straightforward to plot negative values.

Under the hood, it automatically checks for them. If no negative values are found, all days with 0 or missing data are displayed in light gray (using the color_for_none argument). In this case, it's recommended to use a sequential colormap.

Otherwise, color_for_none is ignored and all cells are colored according to their values. Any missing data is treated as 0 by default, so if you need a different approach, fill in the data before plotting.

import matplotlib.pyplot as plt
import dayplot as dp

df = dp.load_dataset()

# add negative values at some random dates
df.loc[df.sample(n=40, replace=False).index, "values"] *= -1

fig, ax = plt.subplots(figsize=(16, 4))
dp.calendar(
    dates=df["dates"],
    values=df["values"],
    cmap="RdBu", # use a diverging colormap (red -> white -> blue)
    start_date="2024-01-01",
    end_date="2024-12-31",
    ax=ax,
)

Red days are the ones with negative values.

Control colormap scaling

You can set custom boundaries for the colormap using the vmin, vcenter and vmax arguments. In this example, any cell with a value at or below -3 displays in the deepest red hue, 0 is shown in a neutral color (white), and any cell at or above 10 appears in the most intense blue.

This can be used as a convenient way of controlling color mapping when there are outliers.

import matplotlib.pyplot as plt
import dayplot as dp

df = dp.load_dataset()

# add negative values at some random dates
df.loc[df.sample(n=40, replace=False).index, "values"] *= -1

fig, ax = plt.subplots(figsize=(16, 4))
dp.calendar(
    dates=df["dates"],
    values=df["values"],
    cmap="RdBu", # use a diverging colormap (red -> white -> blue)
    start_date="2024-01-01",
    end_date="2024-12-31",
    vmin=-3,
    vcenter=0,
    vmax=10,
    ax=ax,
)