Plotting multiple lineplots on single plot with for loop using matplotlib-CodePudding

I have a list of about 200 dataframes that look like this:

I'm running these dataframes through a for loop that runs a linear regression on each one and generates some error metrics. I'd like to generate a single plot that has all these lines but I'm not sure how to do that. Here is the code for the for loop:

clst_lst = []
i = 0
abs_errors = []
pct_errors = []
fig, ax = plt.subplots(111)
for df in dfs_no_dups:
    if df.empty is False:
        df_for_labels = df.copy(deep = True)
        df_for_labels.reset_index(inplace = True)
        
        X = df.loc[:, df.columns != "SMM"]
        y = df["SMM"]
        
        if len(X) > 10:
            clst_lst.append((df_for_labels['cluster'][0], df_for_labels['Vintage'][0]))
            X_train, X_test, y_train, y_test = train_test_split(X, y, train_size=0.80)
            #Scaling for Models
            columns = X.columns.tolist()
            cols = [c for c in columns if c not in ['CprTarget','cluster','M','Vintage', 'SRCDate', 'YearOfSRC']]
            scaler = StandardScaler()
            X_train = X_train[cols]
            X_test = X_test[cols]
            X_train_scaled = scaler.fit_transform(X_train)
            X_test_scaled = scaler.transform(X_test)
        
       

            lr = LinearRegression()
            lr.fit(X_train_scaled,y_train)
            y_pred = lr.predict(X_test_scaled)

            test_df = X_test.copy(deep = True)

            test_df[['M', 'YearOfSRC', 'Vintage']] = df[['M', 'YearOfSRC', 'Vintage']]

            test_df['Y_M'] = test_df['YearOfSRC'].astype(str)   '-'   test_df['M'].astype(str)   '-01'
            test_df['Y_M'] = pd.to_datetime(test_df['Y_M'])

            
            #convert SMM back to CPR for MAPE calculation
            test_df['pred'] = 100*(1-(y_pred/100-1)**12)
            test_df['actual'] = 100*(1-(y_test/100-1)**12)
            test_df['error'] = test_df.pred-test_df.actual
            
            test_df['abs_error'] = abs(test_df['error'])
            test_df['pct_error'] = abs((test_df.error/test_df.actual)*100)
            abs_errors.append(test_df.abs_error)
            pct_errors.append(test_df.pct_error)

            sns.lineplot(data=test_df, x = 'Y_M', y='pct_error', ax=ax)
            ax.set_title("some title")
            xticks = ax.get_xticklabels()
            ax.set_xticklabels(xticks, rotation=45)

            i =1
        else:
            pass

I want to generate one plot that contains the percentage error lines across all the dataframes. Here is what I get with just that one line for sns.lineplot(...):

I want to be able to modify the fig size, axes (rotate x-axis tick labels), add title. Things like that. I think a fig, ax = plt.subplots(111) setup would make sense, but I'm not sure.

Here's the traceback for the error I'm picking up after implementing the provided solution:

AttributeError                            Traceback (most recent call last)
Input In [179], in <cell line: 6>()
     52             pct_errors.append(test_df.pct_error)
     53 #             sns.lineplot(data=test_df, x = 'Y_M', y='abs_error', color = 'red', label = 'abs error') #try vintage instead of coupon as hue
     54 #             plt.title(f'Absolute Error of Linear Regression for {clst_lst[i]}')
     55 #             plt.legend(loc = 'upper right')
     56 #             plt.show()
     57 
     58             #plt.figure(figsize = (10,8))
---> 60             sns.lineplot(data=test_df, x = 'Y_M', y='pct_error', ax=ax)
     61             ax.set_title("some title")
     62             xticks = ax.get_xticklabels()

File ~\Anaconda3\lib\site-packages\seaborn\_decorators.py:46, in _deprecate_positional_args.<locals>.inner_f(*args, **kwargs)
     36     warnings.warn(
     37         "Pass the following variable{} as {}keyword arg{}: {}. "
     38         "From version 0.12, the only valid positional argument "
   (...)
     43         FutureWarning
     44     )
     45 kwargs.update({k: arg for k, arg in zip(sig.parameters, args)})
---> 46 return f(**kwargs)

File ~\Anaconda3\lib\site-packages\seaborn\relational.py:708, in lineplot(x, y, hue, size, style, data, palette, hue_order, hue_norm, sizes, size_order, size_norm, dashes, markers, style_order, units, estimator, ci, n_boot, seed, sort, err_style, err_kws, legend, ax, **kwargs)
    705 if not p.has_xy_data:
    706     return ax
--> 708 p._attach(ax)
    710 p.plot(ax, kwargs)
    711 return ax

File ~\Anaconda3\lib\site-packages\seaborn\_core.py:1134, in VectorPlotter._attach(self, obj, allowed_types, log_scale)
   1127 # Register with the matplotlib unit conversion machinery
   1128 # Perhaps cleaner to manage our own transform objects?
   1129 # XXX Currently this does not allow "unshared" categorical axes
   1130 # We could add metadata to a FacetGrid and set units based on that.
   1131 # See also comment in comp_data, which only uses a single axes to do
   1132 # its mapping, meaning that it won't handle unshared axes well either.
   1133 for ax in ax_list:
-> 1134     axis = getattr(ax, f"{var}axis")
   1135     seed_data = self.plot_data[var]
   1136     if var_type == "categorical":

AttributeError: 'numpy.ndarray' object has no attribute 'yaxis'

CodePudding user response：

Let's have a look at the docs for sns.lineplot

It says there's a parameter called ax which we can use to graph the plot.

Using your suggestion, we can define said ax and insert it into the sns.lineplot call like so

fig, ax = plt.subplots()

# really big for-loop
...

# add `ax` arg here
sns.lineplot(data=test_df, x = 'Y_M', y='pct_error', ax=ax)#, color = 'green')#, label = 'pct error')

Now we can use the ax object (which has all the sns.lineplot stuff in it) to do the things you want.

Like adding a title

ax.set_title("some title")

or rotate x-labels

xticks = ax.get_xticklabels()
ax.set_xticklabels(xticks, rotation=45)

And whatever else you'd like to do.