Plotting all the (important) drivers in one figure
Does my model result make sense? We check, and usually we check both the largest (most important) companies, and we check randomly. Let’s start with following a simple exmaple from seaborn. As my models are on credit ratings forecast based on various point in time scenarios, I am focusing on line plots.
!(multi facet line plot)[https://seaborn.pydata.org/_images/faceted_lineplot.png]
The “dots” dataset is a typical seabon type “long” dataset, where one of the columns is used for paneling. If our data is wide, then we need to transform (using the melt function) as showned later.
Meaning of the “dots” dataset: reactions of mice to different combinations of stimuli. The dataset includes variables such as the time the mouse spent freezing (a measure of fear), the type of stimulus the mouse was exposed to, and the mouse’s genotype.
import seaborn as sns
sns.set_theme(style="ticks")
dots = sns.load_dataset("dots")
In [5]: dots
Out[5]:
align choice time coherence firing_rate
0 dots T1 -80 0.000 33.190
1 dots T1 -80 3.200 31.692
2 dots T1 -80 6.400 34.280
3 dots T1 -80 12.800 32.632
4 dots T1 -80 25.600 35.060
.. ... ... ... ... ...
843 sacc T2 300 3.200 33.282
844 sacc T2 300 6.400 27.584
845 sacc T2 300 12.800 28.512
846 sacc T2 300 25.600 27.010
847 sacc T2 300 51.200 30.959
palette = sns.color_palette("rocket_r") # Define the palette as a list to specify exact values
sns.relplot(data=dots,
x="time", y="firing_rate",
hue="coherence", col="align",
kind="line", palette=palette,
height=5, aspect=.75, facet_kws=dict(sharex=False),
)
sns.relplot(data=dots,
x="time", y="firing_rate",
hue="coherence", size="choice", col="align",
kind="line", size_order=["T1", "T2"], palette=palette,
height=5, aspect=.75, facet_kws=dict(sharex=False),
)
!(multi facet line plot)[https://seaborn.pydata.org/_images/faceted_lineplot.png]
>>> color_nz = ‘#2CA02C’
>>> color_cp = ‘#D62728’
>>> temp = df[drivers + [‘x’, ‘cate’]].melt(id_vars=+ [‘x’, ‘cate’]) # Reshape the data so that your data is long and can use sns.relplot.
>>> g = sns.relplot(data = temp, x=’x’, y=’value’, col=’variable’, kind = ‘line’, height=5, aspect=.75, facet_kws=dict(sharey=False), col_wrap=3, palette=[color_cp, color_nz])
>>> for ax in g.axes.flatten():
ax.set_title(ax.get_title()[10:])
ax.set_ylabel(“”)
>>> for ax in g.axes.flatten():
ax.set_title(ax.get_title()[10:])
ax.set_ylabel(“”)
>>> plt.suptitle(“%s”%company, fontweight=’bold’)
>>> plt.subplots_adjust(top=0.92, right=0.82, hspace=0.32, wspace=0.2)
>>> plt.savefig(os.path.join(“images”, “%s rating change and drivers.png”%(company))
We can look at another example. It creates a list of lambda functions, with the possible intention of a list of functions as [lamba x: x + 1, lambda x: x + 2, …, lambda x: x + 9]. But unfortunately, in this case, lambda function holds on to the expression of \(i\) and won’t undate it until the end. So we end up with a list of lambda functions, all of them are lamba x: x + 9
incremental = [lambda x: x + i for i in range(10)]
print(incremental[0](2))
# 11
print(incremental[-1](2))
# 11
Reference
Python FAQ Why do lambdas defined in a loop with different values all return the same result?
python accumulate official documentation
Excel lambda-the-ultimatae-excel-worksheet-function
import Levenshtein
def match_names(column1, column2): matches = {} for name1 in column1: min_distance = float(‘inf’) match = None for name2 in column2: distance = Levenshtein.distance(name1, name2) if distance < min_distance: min_distance = distance match = name2 matches[name1] = match return matches
Example usage:
column1 = [“John”, “Jane”, “Michael”] column2 = [“Jon”, “Janet”, “Michele”] matches = match_names(column1, column2)
for name1, match in matches.items(): print(f”{name1} matched with {match}”)