Note

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1. Exploratory Data Analysis

import matplotlib.pyplot as plt

import numpy as np
import seaborn as sns
from dython.nominal import associations

from easy_mpl import plot, boxplot
from easy_mpl.utils import create_subplots
from easy_mpl import pie

from utils import SAVE
from utils import plot_line, plot_boxplot, set_rcParams
from utils import read_data, lineplot, version_info

Since version 1.0, it is not needed to import enable_hist_gradient_boosting anymore. HistGradientBoostingClassifier and HistGradientBoostingRegressor are now stable and can be normally imported from sklearn.ensemble.
WARNING:tensorflow:From /home/docs/checkouts/readthedocs.org/user_builds/isl-env-ai/envs/latest/lib/python3.7/site-packages/ai4water/backend.py:80: The name tf.keras.losses.cosine is deprecated. Please use tf.keras.losses.cosine_similarity instead.

WARNING:tensorflow:From /home/docs/checkouts/readthedocs.org/user_builds/isl-env-ai/envs/latest/lib/python3.7/site-packages/ai4water/backend.py:80: The name tf.keras.losses.cosine_proximity is deprecated. Please use tf.keras.losses.cosine_similarity instead.

WARNING:tensorflow:From /home/docs/checkouts/readthedocs.org/user_builds/isl-env-ai/envs/latest/lib/python3.7/site-packages/ai4water/backend.py:80: The name tf.keras.layers.CuDNNGRU is deprecated. Please use tf.compat.v1.keras.layers.CuDNNGRU instead.

WARNING:tensorflow:From /home/docs/checkouts/readthedocs.org/user_builds/isl-env-ai/envs/latest/lib/python3.7/site-packages/ai4water/backend.py:80: The name tf.keras.layers.CuDNNLSTM is deprecated. Please use tf.compat.v1.keras.layers.CuDNNLSTM instead.

version_info()

python 3.7.17 (default, Jun 25 2023, 21:20:48)
[GCC 11.3.0]
os posix
ai4water 1.07
lightgbm 4.0.0
easy_mpl 0.21.4
SeqMetrics 1.3.4
tensorflow 1.15.0
tensorflow.python.keras.api._v1.keras 2.2.4-tf
numpy 1.19.5
pandas 1.3.5
matplotlib 3.5.3
h5py 2.10.0
sklearn 1.0.2
optuna 3.2.0
skopt 0.9.0
seaborn 0.12.2
shap 0.41.0

set_rcParams()

Whole Data

reading whole data

data = read_data()

print(data.shape)

(466, 34)

data.isna().sum()

season         0
Isolate ID     0
Qnr A          2
Qnr B          0
QnrS          17
IMP            1
NDM            0
BIC            0
KPC            0
OXA48          0
TEM            0
SHV            0
CTX-M          0
MCR-1         26
AMP            0
CAZ            0
AMC            0
CTX            0
FEP            0
ATM            0
MEM            0
CIP            0
C              0
NA             0
CN             0
DO             0
F              0
FOT            0
SXT            0
T  (℃)         0
pH             0
TDS (mg/L)     0
DO (mg/L)      0
TN (mg/L)      0
dtype: int64

data.head()
season Isolate ID Qnr A Qnr B QnrS IMP NDM BIC KPC OXA48 TEM SHV CTX-M MCR-1 AMP CAZ AMC CTX FEP ATM MEM CIP C NA CN DO F FOT SXT T (℃) pH TDS (mg/L) DO (mg/L) TN (mg/L)
0 0 1 - + - + + - - - + - + + R R R R R R R R S R S R S S R 28.6 8.98 934 6.30 3.63
1 0 2 - + + - + - - + + - + + R R R R R R R R S R R I S S R 28.9 8.74 917 5.50 5.67
2 0 3 - + + - - - + + - + - - R R R R R R R R S R S R I S R 29.4 8.98 855 5.90 4.40
3 0 4 - - - + - - + + - + - + R R R R R R R R S R R I S S R 27.6 9.40 976 8.45 9.10
4 0 5 - - - - - - - + - + - - R R R R R R R R S R R I S S R 27.5 9.13 1174 6.23 1.58 


data.tail()
season Isolate ID Qnr A Qnr B QnrS IMP NDM BIC KPC OXA48 TEM SHV CTX-M MCR-1 AMP CAZ AMC CTX FEP ATM MEM CIP C NA CN DO F FOT SXT T (℃) pH TDS (mg/L) DO (mg/L) TN (mg/L)
461 3 114 - - + + - - - - + - - + R R R R R R I R S R S R S S R 36.4 7.68 918 5.8 3.23
462 3 115 - - - - - - + + + + + + R R R R R R R R S R R R S R R 37.7 6.88 563 5.8 4.44
463 3 116 - + - - + - - - + - + + R R R R R S S/R I S S S S S R I 36.7 6.98 978 5.8 1.46
464 3 117 - - - - + - - + + - - - R R R R R R I S S I S S S R S 38.4 5.78 1241 5.1 2.16
465 3 118 - + - + - - - + + - + - R R R R R R I S S S S S S S S 38.1 6.31 972 4.6 1.72


plotting a line plot of numerical features

lineplot(save=SAVE)
eda
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findfont: Font family ['Times New Roman'] not found. Falling back to DejaVu Sans.

plotting a boxplot of numerical features without season

wat_chem_cols = ['T  (℃)', 'pH', 'TDS (mg/L)',
                             'DO (mg/L)', 'TN (mg/L)']

fig, axes = create_subplots(len(wat_chem_cols), figsize=(5,4))

colors = ['#FEE1D2', '#FBBEA3', '#FE9473', '#FB6A4B', '#EE3A2C']
for ax, col, color in zip(axes.flat, wat_chem_cols, colors):
    sns.boxplot(data[col], ax=ax,
                fliersize=0.6,
                color=color,
                orient='h',
                )
    ax.set_xlabel(col)
plt.tight_layout()
if SAVE:
    plt.savefig("results/figures/box_num", bbox_inches="tight", dpi=600)
plt.show()
eda

histogram of numerical features

num_columns = ['T  (℃)', 'pH', 'TDS (mg/L)',
               'DO (mg/L)', 'TN (mg/L)', 'season']

fig, axes = create_subplots(len(num_columns))

if not isinstance(axes, np.ndarray):
    axes = np.array([axes])

for ax, col in zip(axes.flat, num_columns):
    sns.histplot(data[col], ax=ax,
                 )
plt.tight_layout()
if SAVE:
    plt.savefig("results/figures/hist_num", bbox_inches="tight", dpi=600)
plt.show()
eda

Season Wise

SEASONS = {
    0: "Fall",
    1: "Winter",
    2: "Spring",
    3: "Summer"
}

COLUMNS = ['T  (℃)', 'pH', 'TDS (mg/L)', 'DO (mg/L)', 'TN (mg/L)']
groups = data.groupby('season')

f, axes = create_subplots(5, sharex="all")

colors = ['#588F07', '#E79D33', '#34ACAF', '#DD6F6D']

for col, ax in zip(COLUMNS, axes.flatten()):

    for (label, grp), color in zip(groups, colors):

        legend = '__nolabel__'
        if col == 'TN (mg/L)':
            legend = SEASONS[label]

        plot(grp[col].values, color=color, label=legend, ax=ax, show=False)

    ax.set_ylabel(col)
    if col == 'TN (mg/L)':
        ax.legend(loc=(1.2, 0.2))

if SAVE:
    plt.savefig("results/figures/seasonwise_line", bbox_inches="tight", dpi=600)
plt.tight_layout()
plt.show()
eda
findfont: Font family ['Times New Roman'] not found. Falling back to DejaVu Sans.

f, axes = create_subplots(5, sharex="all")

for col, ax in zip(COLUMNS, axes.flatten()):

    data_ = [grp[col].values for _, grp in groups]
    labels = [SEASONS[label] for label, _ in groups]
    boxplot(data_, labels=labels, line_color=colors,
            #fill_color=colors, patch_artist=True,
            ax=ax, show=False,
            flierprops=dict(ms=2.0))

    ax.set_ylabel(col)
    if col in ['TN (mg/L)', 'DO (mg/L)']:
        ax.set_xticks([1, 2, 3, 4])
        ax.set_xticklabels(labels)
plt.tight_layout()
if SAVE:
    plt.savefig("results/figures/seasonwise_box.png", bbox_inches="tight", dpi=600)
plt.show()
eda
xticks (8) and xticklabels (4) dont match

xticks (12) and xticklabels (4) dont match

xticks (16) and xticklabels (4) dont match

xticks (8) and xticklabels (4) dont match

Mean values in each season

axes = data[COLUMNS + ['season']].groupby('season').mean().plot(kind="bar", subplots=True)
for ax in axes:
    ax.set_ylabel(ax.get_title())
    ax.set_title('')
    ax.legend().remove()
plt.xticks([0, 1, 2, 3], SEASONS.values())
plt.tight_layout()
if SAVE:
    plt.savefig("results/figures/season_mean.png", bbox_inches="tight", dpi=600)
plt.show()
eda
_ = associations(
    data.drop(columns=["season", "BIC", "Isolate ID", 'SHV',
                        'KPC', 'NDM', 'IMP', 'QnrS', 'Qnr A']),
    nom_nom_assoc="cramer",
    fmt=".1f",
    figsize=(8, 8),
    plot=False,
    cbar=True
)
plt.tight_layout()
if SAVE:
    plt.savefig("results/figures/corr_cat.png", bbox_inches="tight", dpi=600)
plt.show()
eda
_ = associations(
    data.drop(columns=["season", "BIC", "Isolate ID"]),
    nom_nom_assoc="cramer",
    fmt=".1f",
    figsize=(8, 8),
    plot=False,
    cbar=True,
    annot=False
)
plt.tight_layout()
if SAVE:
    plt.savefig("results/figures/corr_0.png", bbox_inches="tight", dpi=600)
plt.show()
eda
output = associations(
    data.drop(columns=["season", "BIC", "Isolate ID", 'SHV',
                        'KPC', 'NDM', 'IMP', 'QnrS', 'Qnr A']),
    nom_nom_assoc="theil",
    fmt=".1f",
    figsize=(8, 8),
    plot=False,
    cbar=True,
)
plt.tight_layout()
if SAVE:
    plt.savefig("results/figures/corr_0_nom_nom_theil.png", bbox_inches="tight", dpi=600)
plt.show()
eda
_ = associations(
    data.drop(columns=["season", "BIC", "Isolate ID"]),
    nom_nom_assoc="theil",
    fmt=".1f",
    annot=False,
    figsize=(8, 8),
    plot=False,
    cbar=True,
)
plt.tight_layout()
if SAVE:
    plt.savefig("results/figures/corr_0_nom_nom_theil0.png", bbox_inches="tight", dpi=600)
plt.show()
eda

Antibiotics

AMP

feature = 'AMP'
# The unique values in AMP are listed below.
print(f'unique values in {feature}: {data[feature].unique()}')

unique values in AMP: ['R' 'S']

_ = pie(data[feature].values, ax_kws=dict(title=feature), show=False)
if SAVE:
    plt.savefig(f"results/figures/pie_{feature}.png", bbox_inches="tight", dpi=600)
plt.show()
AMP
# Following two plots (line and box plot) shows the distribution of
# enviormental features of the samples where AMP values is 'R'.

plot_line(feature, 'R', save=SAVE)
AMP_R
plot_boxplot(feature, 'R', save=SAVE)
AMP_R
# Following two plots (line and box plot) shows the distribution of
# enviormental features of the samples where AMP values is 'S'.

plot_line(feature, 'S', save=SAVE)
AMP_S
plot_boxplot(feature, 'S', save=SAVE)
AMP_S

CAZ

feature = 'CAZ'

print(f'unique values in {feature}: {data[feature].unique()}')

unique values in CAZ: ['R' 'S' 'I']

_ = pie(data[feature].values, ax_kws=dict(title=feature), show=False)
if SAVE:
    plt.savefig(f"results/figures/pie_{feature}.png", bbox_inches="tight", dpi=600)
plt.show()
CAZ
plot_line(feature, 'R', save=SAVE)
CAZ_R
plot_boxplot(feature, 'R', save=SAVE)
CAZ_R
plot_line(feature, 'S', save=SAVE)
CAZ_S
plot_boxplot(feature, 'S', save=SAVE)
CAZ_S
plot_line(feature, 'I', save=SAVE)
CAZ_I
plot_boxplot(feature, 'I', save=SAVE)
CAZ_I

AMC

feature = 'AMC'

print(f'unique values in {feature}: {data[feature].unique()}')

unique values in AMC: ['R' 'S' 'I']

_ = pie(data[feature].values, ax_kws=dict(title=feature), show=False)
if SAVE:
    plt.savefig(f"results/figures/pie_{feature}.png", bbox_inches="tight", dpi=600)
plt.show()
AMC
plot_line(feature, 'R', save=SAVE)
AMC_R
plot_boxplot(feature, 'R', save=SAVE)
AMC_R
plot_line(feature, 'S', save=SAVE)
AMC_S
plot_boxplot(feature, 'S', save=SAVE)
AMC_S
plot_line(feature, 'I', save=SAVE)
AMC_I
#hlot_boxplot(feature, 'I')

CTX

feature = 'CTX'

print(f'unique values in {feature}: {data[feature].unique()}')

unique values in CTX: ['R' 'S' 'I']

_ = pie(data[feature].values, ax_kws=dict(title=feature), show=False)
if SAVE:
    plt.savefig(f"results/figures/pie_{feature}.png", bbox_inches="tight", dpi=600)
plt.show()
CTX
plot_line(feature, 'R', save=SAVE)
CTX_R
plot_boxplot(feature, 'R', save=SAVE)
CTX_R
plot_line(feature, 'S', save=SAVE)
CTX_S
plot_boxplot(feature, 'S', save=SAVE)
CTX_S
plot_line(feature, 'I', save=SAVE)
CTX_I
plot_boxplot(feature, 'I', save=SAVE)
CTX_I

FEP

feature = 'FEP'

print(f'unique values in {feature}: {data[feature].unique()}')

unique values in FEP: ['R' 'S' 'I']

_ = pie(data[feature].values, ax_kws=dict(title=feature), show=False)
if SAVE:
    plt.savefig(f"results/figures/pie_{feature}.png", bbox_inches="tight", dpi=600)
plt.show()
FEP
plot_line(feature, 'R', save=SAVE)
FEP_R
plot_boxplot(feature, 'R', save=SAVE)
FEP_R
plot_line(feature, 'S', save=SAVE)
FEP_S
plot_boxplot(feature, 'S', save=SAVE)
FEP_S
plot_line(feature, 'I', save=SAVE)
FEP_I
plot_boxplot(feature, 'I', save=SAVE)
FEP_I

ATM

feature = 'ATM'

print(f'unique values in {feature}: {data[feature].unique()}')

unique values in ATM: ['R' 'S' 'I']

_ = pie(data[feature].values, ax_kws=dict(title=feature), show=False)
if SAVE:
    plt.savefig(f"results/figures/pie_{feature}.png", bbox_inches="tight", dpi=600)
plt.show()
ATM
plot_line(feature, 'R', save=SAVE)
ATM_R
plot_boxplot(feature, 'R', save=SAVE)
ATM_R
plot_line(feature, 'S', save=SAVE)
ATM_S
plot_boxplot(feature, 'S', save=SAVE)
ATM_S
plot_line(feature, 'I', save=SAVE)
ATM_I
plot_boxplot(feature, 'I', save=SAVE)
ATM_I

MEM

feature = 'MEM'

print(f'unique values in {feature}: {data[feature].unique()}')

unique values in MEM: ['R' 'S' 'I' 'RR' 'S/R']

_ = pie(data[feature].values, ax_kws=dict(title=feature), show=False)
if SAVE:
    plt.savefig(f"results/figures/pie_{feature}.png", bbox_inches="tight", dpi=600)
plt.show()
MEM
plot_line(feature, 'R', save=SAVE)
MEM_R
plot_boxplot(feature, 'R', save=SAVE)
MEM_R
plot_line(feature, 'S', save=SAVE)
MEM_S
plot_boxplot(feature, 'S', save=SAVE)
MEM_S
plot_line(feature, 'I', save=SAVE)
MEM_I
plot_boxplot(feature, 'I', save=SAVE)
MEM_I
plot_line(feature, 'S/R', save=SAVE)
MEM_S/R
plot_boxplot(feature, 'S/R', save=SAVE)
MEM_S/R
plot_line(feature, 'RR', save=SAVE)
MEM_RR
plot_boxplot(feature, 'RR', save=SAVE)
MEM_RR

CIP

feature = 'CIP'

print(f'unique values in {feature}: {data[feature].unique()}')

unique values in CIP: ['R' 'S' 'I']

_ = pie(data[feature].values, ax_kws=dict(title=feature), show=False)
if SAVE:
    plt.savefig(f"results/figures/pie_{feature}.png", bbox_inches="tight", dpi=600)
plt.show()
CIP
plot_line(feature, 'R', save=SAVE)
CIP_R
plot_boxplot(feature, 'R', save=SAVE)
CIP_R
plot_line(feature, 'S', save=SAVE)
CIP_S
plot_boxplot(feature, 'S', save=SAVE)
CIP_S
plot_line(feature, 'I', save=SAVE)
CIP_I
plot_boxplot(feature, 'I', save=SAVE)
CIP_I

C

feature = 'C'

print(f'unique values in {feature}: {data[feature].unique()}')

unique values in C: ['S' 'R' 'I']

_ = pie(data[feature].values, ax_kws=dict(title=feature), show=False)
if SAVE:
    plt.savefig(f"results/figures/pie_{feature}.png", bbox_inches="tight", dpi=600)
plt.show()
C
plot_line(feature, 'R', save=SAVE)
C_R
plot_boxplot(feature, 'R', save=SAVE)
C_R
plot_line(feature, 'S', save=SAVE)
C_S
plot_boxplot(feature, 'S', save=SAVE)
C_S
plot_line(feature, 'I', save=SAVE)
C_I
plot_boxplot(feature, 'I', save=SAVE)
C_I

NA

feature = 'NA'

print(f'unique values in {feature}: {data[feature].unique()}')

unique values in NA: ['R' 'S' 'I' 'RS']

_ = pie(data[feature].values,  ax_kws=dict(title=feature), show=False)
if SAVE:
    plt.savefig(f"results/figures/pie_{feature}.png", bbox_inches="tight", dpi=600)
plt.show()
NA
plot_line(feature, 'R', save=SAVE)
NA_R
plot_boxplot(feature, 'R', save=SAVE)
NA_R
plot_line(feature, 'S', save=SAVE)
NA_S
plot_boxplot(feature, 'S', save=SAVE)
NA_S
plot_line(feature, 'I', save=SAVE)
NA_I
plot_boxplot(feature, 'I', save=SAVE)
NA_I
plot_line(feature, 'RS', save=SAVE)
NA_RS
plot_boxplot(feature, 'RS', save=SAVE)
NA_RS

CN

feature = 'CN'

print(f'unique values in {feature}: {data[feature].unique()}')

unique values in CN: ['S' 'R' 'I']

_ = pie(data[feature].values, ax_kws=dict(title=feature), show=False)
if SAVE:
    plt.savefig(f"results/figures/pie_{feature}.png", bbox_inches="tight", dpi=600)
plt.show()
CN
plot_line(feature, 'R', save=SAVE)
CN_R
plot_boxplot(feature, 'R', save=SAVE)
CN_R
plot_line(feature, 'S', save=SAVE)
CN_S
plot_boxplot(feature, 'S', save=SAVE)
CN_S
plot_line(feature, 'I', save=SAVE)
CN_I
plot_boxplot(feature, 'I', save=SAVE)
CN_I

DO

feature = 'DO'

print(f'unique values in {feature}: {data[feature].unique()}')

unique values in DO: ['R' 'I' 'S']

_ = pie(data[feature].values, ax_kws=dict(title=feature), show=False)
if SAVE:
    plt.savefig(f"results/figures/pie_{feature}.png", bbox_inches="tight", dpi=600)
plt.show()
DO
plot_line(feature, 'R', save=SAVE)
DO_R
plot_boxplot(feature, 'R', save=SAVE)
DO_R
plot_line(feature, 'S', save=SAVE)
DO_S
plot_boxplot(feature, 'S', save=SAVE)
DO_S
plot_line(feature, 'I', save=SAVE)
DO_I
plot_boxplot(feature, 'I', save=SAVE)
DO_I

F

feature = 'F'

print(f'unique values in {feature}: {data[feature].unique()}')

unique values in F: ['S' 'I' 'R']

_ = pie(data[feature].values, ax_kws=dict(title=feature), show=False)
if SAVE:
    plt.savefig(f"results/figures/pie_{feature}.png", bbox_inches="tight", dpi=600)
plt.show()
F
plot_line(feature, 'R', save=SAVE)
F_R
plot_boxplot(feature, 'R', save=SAVE)
F_R
plot_line(feature, 'S', save=SAVE)
F_S
plot_boxplot(feature, 'S', save=SAVE)
F_S
plot_line(feature, 'I', save=SAVE)
F_I
plot_boxplot(feature, 'I', save=SAVE)
F_I

FOT

feature = 'FOT'

print(f'unique values in {feature}: {data[feature].unique()}')

unique values in FOT: ['S' 'R' 'I']

_ = pie(data[feature].values, ax_kws=dict(title=feature), show=False)
if SAVE:
    plt.savefig(f"results/figures/pie_{feature}.png", bbox_inches="tight", dpi=600)
plt.show()
FOT
plot_line(feature, 'R', save=SAVE)
FOT_R
plot_boxplot(feature, 'R', save=SAVE)
FOT_R
plot_line(feature, 'S', save=SAVE)
FOT_S
plot_boxplot(feature, 'S', save=SAVE)
FOT_S
plot_line(feature, 'I', save=SAVE)
FOT_I
plot_boxplot(feature, 'I', save=SAVE)
FOT_I

SXT

feature = 'SXT'

print(f'unique values in {feature}: {data[feature].unique()}')

unique values in SXT: ['R' 'S' 'I']

_ = pie(data[feature].values, ax_kws=dict(title=feature), show=False)
if SAVE:
    plt.savefig(f"results/figures/pie_{feature}.png", bbox_inches="tight", dpi=600)
plt.show()
SXT
plot_line(feature, 'R', save=SAVE)
SXT_R
plot_boxplot(feature, 'R', save=SAVE)
SXT_R
plot_line(feature, 'S', save=SAVE)
SXT_S
plot_boxplot(feature, 'S', save=SAVE)
SXT_S
plot_line(feature, 'I', save=SAVE)
SXT_I
plot_boxplot(feature, 'I', save=SAVE)
SXT_I

Genes

Qnr A

feature = 'Qnr A'

print(f'unique values in {feature}: {data[feature].unique()}')

unique values in Qnr A: ['-' '+' nan]

_ = pie(data[feature].dropna().values, ax_kws=dict(title=feature), show=False)
if SAVE:
    plt.savefig(f"results/figures/pie_{feature}.png", bbox_inches="tight", dpi=600)
plt.show()
Qnr A
plot_line(feature, '+', save=SAVE)
Qnr A_+
plot_boxplot(feature, '+', save=SAVE)
Qnr A_+
plot_line(feature, '-')
Qnr A_-
plot_boxplot(feature, '-', save=SAVE)
Qnr A_-

Qnr B

feature = 'Qnr B'

print(f'unique values in {feature}: {data[feature].unique()}')

unique values in Qnr B: ['+' '-']

_ = pie(data[feature].dropna().values, ax_kws=dict(title=feature), show=False)
if SAVE:
    plt.savefig(f"results/figures/pie_{feature}.png", bbox_inches="tight", dpi=600)
plt.show()
Qnr B
plot_line(feature, '+', save=SAVE)
Qnr B_+
plot_boxplot(feature, '+', save=SAVE)
Qnr B_+
plot_line(feature, '-')
Qnr B_-
plot_boxplot(feature, '-', save=SAVE)
Qnr B_-

QnrS

feature = 'QnrS'

print(f'unique values in {feature}: {data[feature].unique()}')

unique values in QnrS: ['-' '+' nan]

_ = pie(data[feature].dropna().values, ax_kws=dict(title=feature), show=False)
if SAVE:
    plt.savefig(f"results/figures/pie_{feature}.png", bbox_inches="tight", dpi=600)
plt.show()
QnrS
plot_line(feature, '+', save=SAVE)
QnrS_+
plot_boxplot(feature, '+', save=SAVE)
QnrS_+
plot_line(feature, '-', save=SAVE)
QnrS_-
plot_boxplot(feature, '-', save=SAVE)
QnrS_-

IMP

feature = 'IMP'

print(f'unique values in {feature}: {data[feature].unique()}')

unique values in IMP: ['+' '-' nan]

_ = pie(data[feature].dropna().values, ax_kws=dict(title=feature), show=False)
if SAVE:
    plt.savefig(f"results/figures/pie_{feature}.png", bbox_inches="tight", dpi=600)
plt.show()
IMP
plot_line(feature, '+', save=SAVE)
IMP_+
plot_boxplot(feature, '+', save=SAVE)
IMP_+
plot_line(feature, '-', save=SAVE)
IMP_-
plot_boxplot(feature, '-', save=SAVE)
IMP_-

NDM

feature = 'NDM'

print(f'unique values in {feature}: {data[feature].unique()}')

unique values in NDM: ['+' '-']

_ = pie(data[feature].dropna().values, ax_kws=dict(title=feature),
        show=False)
if SAVE:
    plt.savefig(f"results/figures/pie_{feature}.png", bbox_inches="tight", dpi=600)
plt.show()
NDM
plot_line(feature, '+', save=SAVE)
NDM_+
plot_boxplot(feature, '+', save=SAVE)
NDM_+
plot_line(feature, '-')
NDM_-
plot_boxplot(feature, '-', save=SAVE)
NDM_-

BIC

feature = 'BIC'

print(f'unique values in {feature}: {data[feature].unique()}')

unique values in BIC: ['-']

_ = pie(data[feature].dropna().values, ax_kws=dict(title=feature),
        show=False)
if SAVE:
    plt.savefig(f"results/figures/pie_{feature}.png", bbox_inches="tight", dpi=600)
plt.show()
BIC
plot_line(feature, '-', save=SAVE)
BIC_-
plot_boxplot(feature, '-', save=SAVE)
BIC_-

KPC

feature = 'KPC'

print(f'unique values in {feature}: {data[feature].unique()}')

unique values in KPC: ['-' '+']

_ = pie(data[feature].dropna().values, ax_kws=dict(title=feature), show=False)
if SAVE:
    plt.savefig(f"results/figures/pie_{feature}.png", bbox_inches="tight", dpi=600)
plt.show()
KPC
plot_line(feature, '+', save=SAVE)
KPC_+
plot_boxplot(feature, '+', save=SAVE)
KPC_+
plot_line(feature, '-', save=SAVE)
KPC_-
plot_boxplot(feature, '-', save=SAVE)
KPC_-

OXA48

feature = 'OXA48'

print(f'unique values in {feature}: {data[feature].unique()}')

unique values in OXA48: ['-' '+']

_ = pie(data[feature].dropna().values, ax_kws=dict(title=feature), show=False)
if SAVE:
    plt.savefig(f"results/figures/pie_{feature}.png", bbox_inches="tight", dpi=600)
plt.show()
OXA48
plot_line(feature, '+', save=SAVE)
OXA48_+
plot_boxplot(feature, '+', save=SAVE)
OXA48_+
plot_line(feature, '-', save=SAVE)
OXA48_-
plot_boxplot(feature, '-', save=SAVE)
OXA48_-

TEM

feature = 'TEM'

print(f'unique values in {feature}: {data[feature].unique()}')

unique values in TEM: ['+' '-']

_ = pie(data[feature].dropna().values, ax_kws=dict(title=feature), show=False)
if SAVE:
    plt.savefig(f"results/figures/pie_{feature}.png", bbox_inches="tight", dpi=600)
plt.show()
TEM
plot_line(feature, '+', save=SAVE)
TEM_+
plot_boxplot(feature, '+', save=SAVE)
TEM_+
plot_line(feature, '-', save=SAVE)
TEM_-
plot_boxplot(feature, '-', save=SAVE)
TEM_-

SHV

feature = 'SHV'

print(f'unique values in {feature}: {data[feature].unique()}')

unique values in SHV: ['-' '+']

_ = pie(data[feature].dropna().values, ax_kws=dict(title=feature), show=False)
if SAVE:
    plt.savefig(f"results/figures/pie_{feature}.png", bbox_inches="tight", dpi=600)
plt.show()
SHV
plot_line(feature, '+', save=SAVE)
SHV_+
plot_boxplot(feature, '+', save=SAVE)
SHV_+
plot_line(feature, '-', save=SAVE)
SHV_-
plot_boxplot(feature, '-', save=SAVE)
SHV_-

CTX-M

feature = 'CTX-M'

print(f'unique values in {feature}: {data[feature].unique()}')

unique values in CTX-M: ['+' '-']

_ = pie(data[feature].dropna().values, ax_kws=dict(title=feature), show=False)
if SAVE:
    plt.savefig(f"results/figures/pie_{feature}.png", bbox_inches="tight", dpi=600)
plt.show()
CTX-M
plot_line(feature, '+', save=SAVE)
CTX-M_+
plot_boxplot(feature, '+', save=SAVE)
CTX-M_+
plot_line(feature, '-', save=SAVE)
CTX-M_-
plot_boxplot(feature, '-', save=SAVE)
CTX-M_-

MCR-1

feature = 'MCR-1'

print(f'unique values in {feature}: {data[feature].unique()}')

unique values in MCR-1: ['+' '-' nan]

_ = pie(data[feature].dropna().values, ax_kws=dict(title=feature), show=False)
if SAVE:
    plt.savefig(f"results/figures/pie_{feature}.png", bbox_inches="tight", dpi=600)
plt.show()
MCR-1
plot_line(feature, '+', save=SAVE)
MCR-1_+
plot_boxplot(feature, '+', save=SAVE)
MCR-1_+
plot_line(feature, '-', save=SAVE)
MCR-1_-
plot_boxplot(feature, '-', save=SAVE)
MCR-1_-

Total running time of the script: ( 4 minutes 44.328 seconds)

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