""" ========================= Exploratory Data Analysis ========================= """ import os import pandas as pd import matplotlib.pyplot as plt from easy_mpl import imshow from easy_mpl.utils import create_subplots from mne.viz import circular_layout from mne_connectivity.viz import plot_connectivity_circle from utils import SAVE from utils import LABEL_MAP from utils import set_rcParams from utils import distribution_plot from utils import pie_from_series from utils import merge_uniques from utils import prepare_data from utils import print_version_info # %% # Print the version info of the packages being used print_version_info() # %% set_rcParams() # %% fpath = os.path.join("../data/data.xlsx") df = pd.read_excel(fpath) # %% # Display the first 5 rows of the dataset df.head() # %% # Display the last 5 rows of the dataset df.tail() # %% # Display the shape of the dataset df.shape # %% # Display the columns of the dataset df.columns # %% # Display the info of the dataset df.info() # %% # Display the summary statistics of the dataset df.describe() # %% # Display the missing values of the dataset df.isnull().sum() # %% # Display the duplicated rows of the dataset df.duplicated().sum() # %% # numerical columns num_columns = ['Surface area', 'Pore volume', 'BandGap (eV)', 'Au', 'Bi', 'Fe', 'O', 'Catalyst loading (g/L)', 'Light intensity (W)', 'time (min)', 'solution pH', 'Ci (mg/L)', 'Cf (mg/L)', 'Efficiency (%)'] # %% # Display the distribution of the numerical columns for col in num_columns: print(f"{col}: {df[col].describe()}") data_num = df[num_columns].copy() # %% fig, axes = create_subplots(data_num.shape[1]-2, figsize=(9, 8)) for ax, col in zip(axes.flat, data_num.columns): if col in ['Cf (mg/L)', 'Efficiency (%)']: continue distribution_plot(ax=ax, data=data_num[col], box_facecolor='#dcae80', scatter_fc = '#1b1b1c', ridge_lc='#1b1b1c', ) ax.set_xlabel(xlabel=LABEL_MAP.get(col, col), weight='bold', fontsize=14) ax.set_yticklabels('') plt.tight_layout() # plt.savefig("../manuscript/figures/fig2.png", dpi=600, # bbox_inches="tight") plt.show() # %% fig, axes = create_subplots(data_num.shape[1], figsize=(9, 8)) for ax, col in zip(axes.flat, data_num.columns): distribution_plot(ax=ax, data=data_num[col], box_facecolor='#dcae80', scatter_fc = '#1b1b1c', ridge_lc='#1b1b1c', ) ax.set_xlabel(xlabel=LABEL_MAP.get(col, col), weight='bold', fontsize=14) ax.set_yticklabels('') plt.tight_layout() plt.show() # %% # Categorical Data # =================== # %% # categorical columns cat_columns = ['Catalyst type', 'Anions'] # %% # Display the unique values of the categorical columns for col in cat_columns: print(f"{col}: {df[col].unique()}") # %% # Display the value counts of the categorical columns for col in cat_columns: print(f"{col}: {df[col].value_counts()}") merged_series = merge_uniques(df['Catalyst type'], 7) pie_from_series(merged_series, cmap="coolwarm", show=False, leg_pos=(0.85, 0.7)) # %% merged_series = merge_uniques(df['Anions'], 5) pie_from_series(merged_series, cmap="coolwarm", show=False, leg_pos=(0.85, 0.7)) # %% # Correlation # =========== df, cat_enc, an_enc = prepare_data(encoding="ohe", exclude_cf=False) df = df.rename( columns={f"Catalyst type_{idx}":category for idx, category in enumerate(cat_enc.categories_[0])}) df = df.rename( columns={f"Anions_{idx}":category for idx, category in enumerate(an_enc.categories_[0])}) # # %% corr = df.corr(method="pearson") # %% imshow(corr, colorbar=True, show=False) plt.tight_layout() plt.show() # %% df = df.fillna(0.0) # %% node_angles = circular_layout(corr.columns.tolist(), corr.columns.tolist(), start_pos=90, group_boundaries=[0, len(corr.columns.tolist()) // 2]) print(node_angles.shape) # %% fig, ax = plt.subplots(figsize=(16, 16), facecolor="#EFE9E6", subplot_kw=dict(polar=True)) fig, axes = plot_connectivity_circle( corr.values, node_names = corr.columns.tolist(), node_angles=node_angles, fontsize_names =14, fontsize_colorbar =14, facecolor ="#EFE9E6", textcolor='black', #n_lines = 14, node_edgecolor="white", colormap="Spectral", colorbar_size=0.5, colorbar_pos=(-0.5, 0.5), ax=ax) #fig.savefig(f"../manuscript/figures/fig3.png", dpi=600, bbox_inches="tight") fig.tight_layout() # %% df, _, _ = prepare_data(encoding="le", exclude_cf=False) # %% corr = df.corr(method="pearson") # %% imshow(corr, colorbar=True, show=False) plt.tight_layout() plt.show() # %% df = df.fillna(0.0) # %% node_angles = circular_layout(corr.columns.tolist(), corr.columns.tolist(), start_pos=90, group_boundaries=[0, len(corr.columns.tolist()) // 2]) print(node_angles.shape) # %% fig, ax = plt.subplots(figsize=(16, 16), facecolor="#EFE9E6", subplot_kw=dict(polar=True)) fig, axes = plot_connectivity_circle( corr.values, node_names = corr.columns.tolist(), node_angles=node_angles, fontsize_names =14, fontsize_colorbar =14, facecolor ="#EFE9E6", textcolor='black', #n_lines = 14, node_edgecolor="white", colormap="Spectral", colorbar_size=0.5, colorbar_pos=(-0.5, 0.5), ax=ax) # fig.savefig(f"figures/chord_large_le", dpi=600, bbox_inches="tight") fig.tight_layout() # %% df_org = pd.read_excel(fpath) print(df_org.shape) df_org = df_org.drop(columns=['Catalyst type', 'Anions']) corr = df_org.corr(method="pearson") imshow(corr, colorbar=True, show=False) plt.tight_layout() plt.show() # %% df_org = df_org.fillna(0.0) # %% node_angles = circular_layout(corr.columns.tolist(), corr.columns.tolist(), start_pos=90, group_boundaries=[0, len(corr.columns.tolist()) // 2]) print(node_angles.shape) # %% fig, ax = plt.subplots(figsize=(16, 16), facecolor="#EFE9E6", subplot_kw=dict(polar=True)) fig, axes = plot_connectivity_circle( corr.values, node_names = corr.columns.tolist(), node_angles=node_angles, fontsize_names =14, fontsize_colorbar =14, facecolor ="#EFE9E6", textcolor='black', #n_lines = 14,conda node_edgecolor="white", colormap="Spectral", colorbar_size=0.5, colorbar_pos=(-0.5, 0.5), ax=ax) if SAVE: fig.savefig(f"figures/chord_large_org", dpi=600, bbox_inches="tight") fig.tight_layout()