diff --git a/data/tests/fake_associations.csv b/data/tests/fake_associations.csv
deleted file mode 100644
index 94c855f..0000000
--- a/data/tests/fake_associations.csv
+++ /dev/null
@@ -1,9 +0,0 @@
-,trans_id,account_id,trans_amount,balance_after_trans,trans_type,trans_operation,trans_k_symbol,trans_date
-trans_id,1.0,0.474643558789561,-0.2133469591023116,0.0140540821018296,0.4929328532971835,0.8514759788482767,0.8714152457284374,-0.02928925780579595
-account_id,0.474643558789561,1.0,0.024214269962367224,0.0671321717073495,0.05505763017128429,0.10541730588406732,0.20035158555476978,-0.026417221145424455
-trans_amount,-0.2133469591023116,0.024214269962367224,1.0,0.41049495087160576,0.14384781147688352,0.47075620299312654,0.44913033115620205,-0.05135894989826789
-balance_after_trans,0.0140540821018296,0.0671321717073495,0.41049495087160576,1.0,0.10800536670088424,0.20073482566116083,0.13085896079274767,0.03224007887636768
-trans_type,0.4929328532971835,0.05505763017128429,0.14384781147688352,0.10800536670088424,1.0,0.6997816191537789,0.5221930686175845,0.056104192679759315
-trans_operation,0.8514759788482767,0.10541730588406732,0.47075620299312654,0.20073482566116083,0.6997816191537789,1.0,0.6677368630738517,0.09200128175029092
-trans_k_symbol,0.8714152457284374,0.20035158555476978,0.44913033115620205,0.13085896079274767,0.5221930686175845,0.6677368630738517,1.0,0.14896840029863861
-trans_date,-0.02928925780579595,-0.026417221145424455,-0.05135894989826789,0.03224007887636768,0.056104192679759315,0.09200128175029092,0.14896840029863861,1.0
diff --git a/data/tests/fake_associations.parquet b/data/tests/fake_associations.parquet
new file mode 100644
index 0000000..11b1c62
Binary files /dev/null and b/data/tests/fake_associations.parquet differ
diff --git a/data/tests/fake_associations_theil.csv b/data/tests/fake_associations_theil.csv
deleted file mode 100644
index 7b5e658..0000000
--- a/data/tests/fake_associations_theil.csv
+++ /dev/null
@@ -1,9 +0,0 @@
-,trans_id,account_id,trans_amount,balance_after_trans,trans_type,trans_operation,trans_k_symbol,trans_date
-trans_id,1.0,0.474643558789561,-0.2133469591023116,0.0140540821018296,0.4929328532971835,0.8514759788482767,0.8714152457284374,-0.02928925780579595
-account_id,0.474643558789561,1.0,0.024214269962367224,0.0671321717073495,0.05505763017128429,0.10541730588406732,0.20035158555476978,-0.026417221145424455
-trans_amount,-0.2133469591023116,0.024214269962367224,1.0,0.41049495087160576,0.14384781147688352,0.47075620299312654,0.44913033115620205,-0.05135894989826789
-balance_after_trans,0.0140540821018296,0.0671321717073495,0.41049495087160576,1.0,0.10800536670088424,0.20073482566116083,0.13085896079274767,0.03224007887636768
-trans_type,0.4929328532971835,0.05505763017128429,0.14384781147688352,0.10800536670088424,1.0,0.8307948936127061,0.4751127792537729,0.056104192679759315
-trans_operation,0.8514759788482767,0.10541730588406732,0.47075620299312654,0.20073482566116083,0.4315720065489207,1.0,0.6060246958534777,0.09200128175029092
-trans_k_symbol,0.8714152457284374,0.20035158555476978,0.44913033115620205,0.13085896079274767,0.2502006364307283,0.6143594912151971,1.0,0.14896840029863861
-trans_date,-0.02928925780579595,-0.026417221145424455,-0.05135894989826789,0.03224007887636768,0.056104192679759315,0.09200128175029092,0.14896840029863861,1.0
diff --git a/data/tests/fake_associations_theil.parquet b/data/tests/fake_associations_theil.parquet
new file mode 100644
index 0000000..c7e5524
Binary files /dev/null and b/data/tests/fake_associations_theil.parquet differ
diff --git a/data/tests/fake_test_sample_numerical_encoded.csv b/data/tests/fake_test_sample_numerical_encoded.csv
deleted file mode 100644
index 5262ad9..0000000
--- a/data/tests/fake_test_sample_numerical_encoded.csv
+++ /dev/null
@@ -1,1001 +0,0 @@
-trans_id,account_id,trans_amount,balance_after_trans,trans_type_CREDIT,trans_type_UNKNOWN,trans_type_WITHDRAWAL,trans_operation_CC_WITHDRAWAL,trans_operation_COLLECTION_FROM_OTHER_BANK,trans_operation_CREDIT_IN_CASH,trans_operation_REMITTANCE_TO_OTHER_BANK,trans_operation_UNKNOWN,trans_operation_WITHDRAWAL_IN_CASH,trans_k_symbol_HOUSEHOLD,trans_k_symbol_INSURANCE_PAYMENT,trans_k_symbol_INTEREST_CREDITED,trans_k_symbol_LOAN_PAYMENT,trans_k_symbol_OLD_AGE_PENSION,trans_k_symbol_PAYMENT_FOR_STATEMENT,trans_k_symbol_SANCTION_INTEREST,trans_k_symbol_UNKNOWN,trans_date
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diff --git a/data/tests/fake_test_sample_numerical_encoded.parquet b/data/tests/fake_test_sample_numerical_encoded.parquet
new file mode 100644
index 0000000..7b54734
Binary files /dev/null and b/data/tests/fake_test_sample_numerical_encoded.parquet differ
diff --git a/data/tests/real_associations.csv b/data/tests/real_associations.csv
deleted file mode 100644
index ca10882..0000000
--- a/data/tests/real_associations.csv
+++ /dev/null
@@ -1,9 +0,0 @@
-,trans_id,account_id,trans_amount,balance_after_trans,trans_type,trans_operation,trans_k_symbol,trans_date
-trans_id,1.0,0.5228300496676787,-0.22033993565166987,0.028875713717958783,0.47428712569977377,0.8352915293991163,0.8414511312320141,0.054573743204015576
-account_id,0.5228300496676787,1.0,0.025829382873738,0.10896692701953438,0.04827916714843414,0.08816937558698971,0.16462397499373396,0.05213217137068296
-trans_amount,-0.22033993565166987,0.025829382873738,1.0,0.39474608564158536,0.19389971128773834,0.5311580367062264,0.4949401219382614,0.01663905287694896
-balance_after_trans,0.028875713717958783,0.10896692701953438,0.39474608564158536,1.0,0.12419170529475135,0.23006188182617215,0.1781853887887315,0.12393532301006098
-trans_type,0.47428712569977377,0.04827916714843414,0.19389971128773834,0.12419170529475135,1.0,0.7092204023979536,0.5164395860852621,0.031496012385482906
-trans_operation,0.8352915293991163,0.08816937558698971,0.5311580367062264,0.23006188182617215,0.7092204023979536,1.0,0.6631455360238371,0.0930897099137863
-trans_k_symbol,0.8414511312320141,0.16462397499373396,0.4949401219382614,0.1781853887887315,0.5164395860852621,0.6631455360238371,1.0,0.10794487843160071
-trans_date,0.054573743204015576,0.05213217137068296,0.01663905287694896,0.12393532301006098,0.031496012385482906,0.0930897099137863,0.10794487843160071,1.0
diff --git a/data/tests/real_associations.parquet b/data/tests/real_associations.parquet
new file mode 100644
index 0000000..d554d7f
Binary files /dev/null and b/data/tests/real_associations.parquet differ
diff --git a/data/tests/real_associations_theil.csv b/data/tests/real_associations_theil.csv
deleted file mode 100644
index 9367ce8..0000000
--- a/data/tests/real_associations_theil.csv
+++ /dev/null
@@ -1,9 +0,0 @@
-,trans_id,account_id,trans_amount,balance_after_trans,trans_type,trans_operation,trans_k_symbol,trans_date
-trans_id,1.0,0.5228300496676787,-0.22033993565166987,0.028875713717958783,0.47428712569977377,0.8352915293991163,0.8414511312320141,0.054573743204015576
-account_id,0.5228300496676787,1.0,0.025829382873738,0.10896692701953438,0.04827916714843414,0.08816937558698971,0.16462397499373396,0.05213217137068296
-trans_amount,-0.22033993565166987,0.025829382873738,1.0,0.39474608564158536,0.19389971128773834,0.5311580367062264,0.4949401219382614,0.01663905287694896
-balance_after_trans,0.028875713717958783,0.10896692701953438,0.39474608564158536,1.0,0.12419170529475135,0.23006188182617215,0.1781853887887315,0.12393532301006098
-trans_type,0.47428712569977377,0.04827916714843414,0.19389971128773834,0.12419170529475135,1.0,0.9044741348299755,0.4820483704707144,0.031496012385482906
-trans_operation,0.8352915293991163,0.08816937558698971,0.5311580367062264,0.23006188182617215,0.4591498022313355,1.0,0.6041794163417048,0.0930897099137863
-trans_k_symbol,0.8414511312320141,0.16462397499373396,0.4949401219382614,0.1781853887887315,0.25020452459692694,0.6177492339768428,1.0,0.10794487843160071
-trans_date,0.054573743204015576,0.05213217137068296,0.01663905287694896,0.12393532301006098,0.031496012385482906,0.0930897099137863,0.10794487843160071,1.0
diff --git a/data/tests/real_associations_theil.parquet b/data/tests/real_associations_theil.parquet
new file mode 100644
index 0000000..d88ef17
Binary files /dev/null and b/data/tests/real_associations_theil.parquet differ
diff --git a/data/tests/real_test_sample_numerical_encoded.csv b/data/tests/real_test_sample_numerical_encoded.csv
deleted file mode 100644
index 8c026ea..0000000
--- a/data/tests/real_test_sample_numerical_encoded.csv
+++ /dev/null
@@ -1,1001 +0,0 @@
-trans_id,account_id,trans_amount,balance_after_trans,trans_type_CREDIT,trans_type_UNKNOWN,trans_type_WITHDRAWAL,trans_operation_CC_WITHDRAWAL,trans_operation_COLLECTION_FROM_OTHER_BANK,trans_operation_CREDIT_IN_CASH,trans_operation_REMITTANCE_TO_OTHER_BANK,trans_operation_UNKNOWN,trans_operation_WITHDRAWAL_IN_CASH,trans_k_symbol_HOUSEHOLD,trans_k_symbol_INSURANCE_PAYMENT,trans_k_symbol_INTEREST_CREDITED,trans_k_symbol_LOAN_PAYMENT,trans_k_symbol_OLD_AGE_PENSION,trans_k_symbol_PAYMENT_FOR_STATEMENT,trans_k_symbol_SANCTION_INTEREST,trans_k_symbol_UNKNOWN,trans_date
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-957189,3261,200.0,200.0,1,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,1,187
-614297,2094,3433.0,22069.4,1,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,1807
-100523,344,13948.0,45670.7,1,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,1,1008
-867413,2953,580.0,55544.5,0,0,1,0,0,0,1,0,0,1,0,0,0,0,0,0,0,1982
-3645743,3497,168.9,40098.9,1,0,0,0,0,0,0,1,0,0,0,1,0,0,0,0,0,1764
-864928,2944,615.0,30759.6,0,0,1,0,0,0,1,0,0,0,0,0,0,0,0,0,1,1769
-496484,1692,14.6,14666.3,0,0,1,0,0,0,0,0,1,0,0,0,0,0,1,0,0,1338
-388543,1321,6037.5,25559.2,1,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,1,1616
-805045,2742,14.6,46372.6,0,0,1,0,0,0,0,0,1,0,0,0,0,0,1,0,0,1945
-1245096,4259,10800.0,69256.4,0,0,1,0,0,0,0,0,1,0,0,0,0,0,0,0,1,2151
-500732,1706,3087.0,52203.0,1,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,1684
-2505133,8268,5870.0,26527.6,0,0,1,0,0,0,1,0,0,0,0,0,0,0,0,0,1,1800
-441718,1501,2292.0,31289.6,0,0,1,0,0,0,1,0,0,1,0,0,0,0,0,0,0,1989
-2583286,8533,14.6,123022.5,0,0,1,0,0,0,0,0,1,0,0,0,0,0,1,0,0,1246
-2956243,9790,5464.0,88029.5,1,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,1,2066
-1549003,5270,1800.0,70655.2,0,0,1,0,0,0,0,0,1,0,0,0,0,0,0,0,1,551
diff --git a/data/tests/real_test_sample_numerical_encoded.feather b/data/tests/real_test_sample_numerical_encoded.feather
new file mode 100644
index 0000000..77e18af
Binary files /dev/null and b/data/tests/real_test_sample_numerical_encoded.feather differ
diff --git a/data/tests/real_test_sample_numerical_encoded.parquet b/data/tests/real_test_sample_numerical_encoded.parquet
new file mode 100644
index 0000000..a9a756e
Binary files /dev/null and b/data/tests/real_test_sample_numerical_encoded.parquet differ
diff --git a/requirements.txt b/requirements.txt
index 8f82577..e32599d 100644
--- a/requirements.txt
+++ b/requirements.txt
@@ -1,8 +1,9 @@
 dython==0.7.3
 scipy
-pandas==1.5.*
+pandas==2.0.*
 seaborn
 numpy
 matplotlib
 tqdm
-scikit-learn
\ No newline at end of file
+scikit-learn
+pyarrow
\ No newline at end of file
diff --git a/setup.py b/setup.py
index df07d7e..37b356d 100644
--- a/setup.py
+++ b/setup.py
@@ -5,7 +5,7 @@
 
 setuptools.setup(
     name="table-evaluator",
-    version="v1.5.0",
+    version="v1.6.0",
     author="Bauke Brenninkmeijer",
     author_email="bauke.brenninkmeijer@gmail.com",
     description="A package to evaluate how close a synthetic data set is to real data.",
@@ -14,7 +14,7 @@
     url="https://github.com/Baukebrenninkmeijer/Table-Evaluator",
     packages=setuptools.find_packages(),
     install_requires=[
-        'pandas==1.5.*',
+        'pandas==2.0.*',
         'numpy',
         'tqdm',
         'psutil',
diff --git a/table_evaluator/table_evaluator.py b/table_evaluator/table_evaluator.py
index 289cd1f..8570e8b 100644
--- a/table_evaluator/table_evaluator.py
+++ b/table_evaluator/table_evaluator.py
@@ -1,28 +1,30 @@
 import copy
 import os
 import warnings
-import pandas as pd
+from pathlib import Path
+from typing import Dict, Tuple, Union
+
+import matplotlib.pyplot as plt
 import numpy as np
+import pandas as pd
 import seaborn as sns
-import matplotlib.pyplot as plt
-from pathlib import Path
-from tqdm import tqdm
+from dython.nominal import associations, numerical_encoding
 from scipy import stats
-from typing import Tuple, Dict, Union
 from scipy.spatial.distance import cdist
-from sklearn.model_selection import KFold
-from sklearn.tree import DecisionTreeClassifier
-from sklearn.neural_network import MLPClassifier
 from sklearn.decomposition import PCA
-from sklearn.metrics import f1_score, mean_squared_error, jaccard_score
+from sklearn.ensemble import RandomForestClassifier, RandomForestRegressor
 from sklearn.exceptions import ConvergenceWarning
-from sklearn.ensemble import RandomForestRegressor, RandomForestClassifier
-from sklearn.linear_model import Lasso, Ridge, ElasticNet, LogisticRegression
-from dython.nominal import associations, numerical_encoding
-from .viz import *
+from sklearn.linear_model import ElasticNet, Lasso, LogisticRegression, Ridge
+from sklearn.metrics import f1_score, jaccard_score, mean_squared_error
+from sklearn.model_selection import KFold
+from sklearn.neural_network import MLPClassifier
+from sklearn.tree import DecisionTreeClassifier
+from tqdm import tqdm
+
 from .metrics import *
-from .notebook import visualize_notebook, isnotebook, EvaluationResult
+from .notebook import EvaluationResult, isnotebook, visualize_notebook
 from .utils import dict_to_df
+from .viz import *
 
 
 class TableEvaluator:
@@ -60,7 +62,7 @@ def __init__(self, real: pd.DataFrame, fake: pd.DataFrame, cat_cols=None, unique
         if cat_cols is None:
             real = real.infer_objects()
             fake = fake.infer_objects()
-            self.numerical_columns = [column for column in real._get_numeric_data().columns if
+            self.numerical_columns = [column for column in real.select_dtypes(include='number').columns if
                                       len(real[column].unique()) > unique_thresh]
             self.categorical_columns = [column for column in real.columns if column not in self.numerical_columns]
         else:
@@ -119,9 +121,14 @@ def plot_cumsums(self, nr_cols=4, fname=None):
         fig.suptitle('Cumulative Sums per feature', fontsize=16)
         axes = ax.flatten()
         for i, col in enumerate(self.real.columns):
-            r = self.real[col]
-            f = self.fake.iloc[:, self.real.columns.tolist().index(col)]
-            cdf(r, f, col, 'Cumsum', ax=axes[i])
+            try:
+                r = self.real[col]
+                f = self.fake.iloc[:, self.real.columns.tolist().index(col)]
+                cdf(r, f, col, 'Cumsum', ax=axes[i])
+            except Exception as e:
+                print(f'Error while plotting column {col}')
+                raise e
+
         plt.tight_layout(rect=[0, 0.02, 1, 0.98])
 
         if fname is not None:
@@ -153,7 +160,7 @@ def plot_distributions(self, nr_cols=3, fname=None):
         axes = ax.flatten()
         for i, col in enumerate(self.real.columns):
             if col not in self.categorical_columns:
-                plot_df = pd.DataFrame({col: self.real[col].append(self.fake[col]), 'kind': ['real'] * self.n_samples + ['fake'] * self.n_samples})
+                plot_df = pd.DataFrame({col: pd.concat([self.real[col], self.fake[col]], axis=0), 'kind': ['real'] * self.n_samples + ['fake'] * self.n_samples})
                 fig = sns.histplot(plot_df, x=col, hue='kind', ax=axes[i], stat='probability', legend=True)
                 axes[i].set_autoscaley_on(True)
             else:
diff --git a/table_evaluator/viz.py b/table_evaluator/viz.py
index c1b0fa7..9e133cc 100644
--- a/table_evaluator/viz.py
+++ b/table_evaluator/viz.py
@@ -1,9 +1,10 @@
-from dython.nominal import associations
-from typing import Union, List, Optional
-import pandas as pd
+from typing import List, Optional, Union
+
 import matplotlib.pyplot as plt
 import numpy as np
+import pandas as pd
 import seaborn as sns
+from dython.nominal import associations
 
 
 def plot_var_cor(x: Union[pd.DataFrame, np.ndarray], ax=None, return_values: bool = False, **kwargs) -> Optional[np.ndarray]:
@@ -69,9 +70,9 @@ def plot_correlation_difference(real: pd.DataFrame, fake: pd.DataFrame, plot_dif
     else:
         fig, ax = plt.subplots(1, 2, figsize=(20, 8))
 
-    real_corr = associations(real, nominal_columns=cat_cols, plot=False, theil_u=True,
+    real_corr = associations(real, nominal_columns=cat_cols, plot=False, nom_nom_assoc='theil',
                              mark_columns=True, annot=annot, ax=ax[0], cmap=cmap)['corr']
-    fake_corr = associations(fake, nominal_columns=cat_cols, plot=False, theil_u=True,
+    fake_corr = associations(fake, nominal_columns=cat_cols, plot=False, nom_nom_assoc='theil',
                              mark_columns=True, annot=annot, ax=ax[1], cmap=cmap)['corr']
 
     if plot_diff:
@@ -86,7 +87,7 @@ def plot_correlation_difference(real: pd.DataFrame, fake: pd.DataFrame, plot_dif
         ax[i].set_title(label, **title_font)
     plt.tight_layout()
 
-    if fname is not None: 
+    if fname is not None:
         plt.savefig(fname)
 
     plt.show()
@@ -105,12 +106,12 @@ def plot_correlation_comparison(evaluators: List, annot=False):
     flat_ax = ax.flatten()
     flat_ax[nr_plots + 1].clear()
     fake_corr = []
-    real_corr = associations(evaluators[0].real, nominal_columns=evaluators[0].categorical_columns, plot=False, theil_u=True,
+    real_corr = associations(evaluators[0].real, nominal_columns=evaluators[0].categorical_columns, plot=False, nom_nom_assoc='theil', compute_only=True,
                              mark_columns=True, annot=False, cmap=cmap, cbar=False, ax=flat_ax[0])['corr']
     for i in range(1, nr_plots):
         cbar = True if i % (nr_plots - 1) == 0 else False
         fake_corr.append(
-            associations(evaluators[i - 1].fake, nominal_columns=evaluators[0].categorical_columns, plot=False, theil_u=True,
+            associations(evaluators[i - 1].fake, nominal_columns=evaluators[0].categorical_columns, plot=False, nom_nom_assoc='theil', compute_only=True,
                          mark_columns=True, annot=False, cmap=cmap, cbar=cbar, ax=flat_ax[i])['corr']
         )
         if i % (nr_plots - 1) == 0:
@@ -145,11 +146,11 @@ def cdf(data_r, data_f, xlabel: str = 'Values', ylabel: str = 'Cumulative Sum',
     :param data_r: Series with real data
     :param data_f: Series with fake data
     :param xlabel: Label to put on the x-axis
-    :param ylabel: Label to put on the y-axis
+    :param ylabel: Label to put  on the y-axis
     :param ax: The axis to plot on. If ax=None, a new figure is created.
     """
-    x1 = np.sort(data_r)
-    x2 = np.sort(data_f)
+    x1 = data_r.sort_values()
+    x2 = data_f.sort_values()
     y = np.arange(1, len(data_r) + 1) / len(data_r)
 
     ax = ax if ax else plt.subplots()[1]
@@ -167,9 +168,10 @@ def cdf(data_r, data_f, xlabel: str = 'Values', ylabel: str = 'Cumulative Sum',
 
     # If labels are strings, rotate them vertical
     if isinstance(data_r, pd.Series) and data_r.dtypes == 'object':
+        all_labels = set(data_r) | set(data_f)
         ticks_loc = ax.get_xticks()
         ax.xaxis.set_major_locator(mticker.FixedLocator(ticks_loc))
-        ax.set_xticklabels(data_r.sort_values().unique(), rotation='vertical')
+        ax.set_xticklabels(sorted(all_labels), rotation='vertical')
 
     if ax is None:
         plt.show()
@@ -201,7 +203,7 @@ def plot_mean_std(real: pd.DataFrame, fake: pd.DataFrame, ax=None, fname=None):
     :param real: DataFrame containing the real data
     :param fake: DataFrame containing the fake data
     :param ax: Axis to plot on. If none, a new figure is made.
-    :param fname: If not none, saves the plot with this file name. 
+    :param fname: If not none, saves the plot with this file name.
     """
     if ax is None:
         fig, ax = plt.subplots(1, 2, figsize=(10, 5))
@@ -209,8 +211,8 @@ def plot_mean_std(real: pd.DataFrame, fake: pd.DataFrame, ax=None, fname=None):
 
     ax[0].grid(True)
     ax[1].grid(True)
-    real = real._get_numeric_data()
-    fake = fake._get_numeric_data()
+    real = real.select_dtypes(include='number')
+    fake = fake.select_dtypes(include='number')
     real_mean = np.log(np.add(abs(real.mean()).values, 1e-5))
     fake_mean = np.log(np.add(abs(fake.mean()).values, 1e-5))
     min_mean = min(real_mean) - 1
diff --git a/tests/create_test_data.ipynb b/tests/create_test_data.ipynb
index f6f2b1b..5e8ab60 100644
--- a/tests/create_test_data.ipynb
+++ b/tests/create_test_data.ipynb
@@ -4,8 +4,18 @@
    "cell_type": "code",
    "execution_count": 1,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "IPython not installed.\n"
+     ]
+    }
+   ],
    "source": [
+    "import sys\n",
+    "sys.path.append('..')\n",
     "import pandas as pd\n",
     "from table_evaluator import *\n",
     "import numpy as np\n",
@@ -20,21 +30,21 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "assert dython.__version__ == '0.7.3', 'Dython version should be version 0.5.1'"
+    "assert dython.__version__ == '0.7.3', 'Dython version should be version 0.7.3'"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": 3,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "'1.5.2'"
+       "'2.0.3'"
       ]
      },
-     "execution_count": 11,
+     "execution_count": 3,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -52,7 +62,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 4,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -63,7 +73,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 5,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -80,7 +90,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 6,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -90,12 +100,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 7,
    "metadata": {},
    "outputs": [],
    "source": [
-    "real_num_enc.to_csv(test_data_folder/'real_test_sample_numerical_encoded.csv', index=False)\n",
-    "fake_num_enc.to_csv(test_data_folder/'fake_test_sample_numerical_encoded.csv', index=False)"
+    "real_num_enc.to_parquet(test_data_folder/'real_test_sample_numerical_encoded.parquet')\n",
+    "fake_num_enc.to_parquet(test_data_folder/'fake_test_sample_numerical_encoded.parquet')"
    ]
   },
   {
@@ -107,7 +117,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": 8,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -120,7 +130,167 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>trans_id</th>\n",
+       "      <th>account_id</th>\n",
+       "      <th>trans_amount</th>\n",
+       "      <th>balance_after_trans</th>\n",
+       "      <th>trans_type</th>\n",
+       "      <th>trans_operation</th>\n",
+       "      <th>trans_k_symbol</th>\n",
+       "      <th>trans_date</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>trans_id</th>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>0.522830</td>\n",
+       "      <td>-0.220340</td>\n",
+       "      <td>0.028876</td>\n",
+       "      <td>0.474287</td>\n",
+       "      <td>0.835292</td>\n",
+       "      <td>0.841451</td>\n",
+       "      <td>0.054574</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>account_id</th>\n",
+       "      <td>0.522830</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>0.025829</td>\n",
+       "      <td>0.108967</td>\n",
+       "      <td>0.048279</td>\n",
+       "      <td>0.088169</td>\n",
+       "      <td>0.164624</td>\n",
+       "      <td>0.052132</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>trans_amount</th>\n",
+       "      <td>-0.220340</td>\n",
+       "      <td>0.025829</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>0.394746</td>\n",
+       "      <td>0.193900</td>\n",
+       "      <td>0.531158</td>\n",
+       "      <td>0.494940</td>\n",
+       "      <td>0.016639</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>balance_after_trans</th>\n",
+       "      <td>0.028876</td>\n",
+       "      <td>0.108967</td>\n",
+       "      <td>0.394746</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>0.124192</td>\n",
+       "      <td>0.230062</td>\n",
+       "      <td>0.178185</td>\n",
+       "      <td>0.123935</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>trans_type</th>\n",
+       "      <td>0.474287</td>\n",
+       "      <td>0.048279</td>\n",
+       "      <td>0.193900</td>\n",
+       "      <td>0.124192</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>0.709220</td>\n",
+       "      <td>0.516440</td>\n",
+       "      <td>0.031496</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>trans_operation</th>\n",
+       "      <td>0.835292</td>\n",
+       "      <td>0.088169</td>\n",
+       "      <td>0.531158</td>\n",
+       "      <td>0.230062</td>\n",
+       "      <td>0.709220</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>0.663146</td>\n",
+       "      <td>0.093090</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>trans_k_symbol</th>\n",
+       "      <td>0.841451</td>\n",
+       "      <td>0.164624</td>\n",
+       "      <td>0.494940</td>\n",
+       "      <td>0.178185</td>\n",
+       "      <td>0.516440</td>\n",
+       "      <td>0.663146</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>0.107945</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>trans_date</th>\n",
+       "      <td>0.054574</td>\n",
+       "      <td>0.052132</td>\n",
+       "      <td>0.016639</td>\n",
+       "      <td>0.123935</td>\n",
+       "      <td>0.031496</td>\n",
+       "      <td>0.093090</td>\n",
+       "      <td>0.107945</td>\n",
+       "      <td>1.000000</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                     trans_id  account_id  trans_amount  balance_after_trans  \\\n",
+       "trans_id             1.000000    0.522830     -0.220340             0.028876   \n",
+       "account_id           0.522830    1.000000      0.025829             0.108967   \n",
+       "trans_amount        -0.220340    0.025829      1.000000             0.394746   \n",
+       "balance_after_trans  0.028876    0.108967      0.394746             1.000000   \n",
+       "trans_type           0.474287    0.048279      0.193900             0.124192   \n",
+       "trans_operation      0.835292    0.088169      0.531158             0.230062   \n",
+       "trans_k_symbol       0.841451    0.164624      0.494940             0.178185   \n",
+       "trans_date           0.054574    0.052132      0.016639             0.123935   \n",
+       "\n",
+       "                     trans_type  trans_operation  trans_k_symbol  trans_date  \n",
+       "trans_id               0.474287         0.835292        0.841451    0.054574  \n",
+       "account_id             0.048279         0.088169        0.164624    0.052132  \n",
+       "trans_amount           0.193900         0.531158        0.494940    0.016639  \n",
+       "balance_after_trans    0.124192         0.230062        0.178185    0.123935  \n",
+       "trans_type             1.000000         0.709220        0.516440    0.031496  \n",
+       "trans_operation        0.709220         1.000000        0.663146    0.093090  \n",
+       "trans_k_symbol         0.516440         0.663146        1.000000    0.107945  \n",
+       "trans_date             0.031496         0.093090        0.107945    1.000000  "
+      ]
+     },
+     "execution_count": 11,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "pd.read_parquet(test_data_folder/'real_associations.csv')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -128,16 +298,16 @@
     "# for i in [real_assoc, real_assoc_theil, fake_assoc, fake_assoc_theil]:\n",
     "#     i.index.rename('index', inplace=True)\n",
     "\n",
-    "real_assoc.to_csv(test_data_folder/'real_associations.csv')\n",
-    "real_assoc_theil.to_csv(test_data_folder/'real_associations_theil.csv')\n",
+    "real_assoc.to_parquet(test_data_folder/'real_associations.parquet')\n",
+    "real_assoc_theil.to_parquet(test_data_folder/'real_associations_theil.parquet')\n",
     "\n",
-    "fake_assoc.to_csv(test_data_folder/'fake_associations.csv')\n",
-    "fake_assoc_theil.to_csv(test_data_folder/'fake_associations_theil.csv')"
+    "fake_assoc.to_parquet(test_data_folder/'fake_associations.parquet')\n",
+    "fake_assoc_theil.to_parquet(test_data_folder/'fake_associations_theil.parquet')"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 13,
    "metadata": {},
    "outputs": [
     {
@@ -286,13 +456,13 @@
        "trans_date             0.031496         0.093090        0.107945    1.000000  "
       ]
      },
-     "execution_count": 9,
+     "execution_count": 13,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "pd.read_csv(test_data_folder/'real_associations_theil.csv', index_col='Unnamed: 0')"
+    "pd.read_parquet(test_data_folder/'real_associations_theil.parquet')"
    ]
   },
   {
diff --git a/tests/metrics_test.py b/tests/metrics_test.py
index 3dc3e22..54d310c 100644
--- a/tests/metrics_test.py
+++ b/tests/metrics_test.py
@@ -1,12 +1,16 @@
-import pytest
-import pandas as pd
-import numpy as np
 import sys
+
+import numpy as np
+import pandas as pd
+import pytest
+
 sys.path.append('..')
+from pathlib import Path
+
+from dython.nominal import associations, numerical_encoding
+
 from table_evaluator.metrics import *
 from table_evaluator.utils import load_data
-from dython.nominal import associations, numerical_encoding
-from pathlib import Path
 
 data_folder = Path('data')
 test_data_folder = Path('data/tests')
@@ -39,10 +43,10 @@ def test_associations():
     Tests that check wether the dython associations are still computed as is expected.
     """
     # load test data
-    real_assoc = pd.read_csv(test_data_folder/'real_associations.csv', index_col='Unnamed: 0')
-    real_assoc_theil = pd.read_csv(test_data_folder/'real_associations_theil.csv', index_col='Unnamed: 0')
-    fake_assoc = pd.read_csv(test_data_folder/'fake_associations.csv', index_col='Unnamed: 0')
-    fake_assoc_theil = pd.read_csv(test_data_folder/'fake_associations_theil.csv', index_col='Unnamed: 0')
+    real_assoc = pd.read_parquet(test_data_folder/'real_associations.parquet')
+    real_assoc_theil = pd.read_parquet(test_data_folder/'real_associations_theil.parquet')
+    fake_assoc = pd.read_parquet(test_data_folder/'fake_associations.parquet')
+    fake_assoc_theil = pd.read_parquet(test_data_folder/'fake_associations_theil.parquet')
 
     # Assert equality with saved data
     pd.testing.assert_frame_equal(real_assoc, associations(real, nominal_columns=cat_cols, compute_only=True)['corr'])
@@ -56,13 +60,9 @@ def test_numerical_encoding():
     Tests that check wether the dython numerical_encoding are still computed as is expected.
     """
     num_encoding = numerical_encoding(real, nominal_columns=cat_cols)
-    uint_cols = num_encoding.select_dtypes(include=['uint8']).columns.tolist()
-    num_encoding[uint_cols] = num_encoding[uint_cols].astype('int64')
-    stored_encoding = pd.read_csv(test_data_folder/'real_test_sample_numerical_encoded.csv')
+    stored_encoding = pd.read_parquet(test_data_folder/'real_test_sample_numerical_encoded.parquet')
     pd.testing.assert_frame_equal(num_encoding, stored_encoding)
 
     num_encoding = numerical_encoding(fake, nominal_columns=cat_cols)
-    uint_cols = num_encoding.select_dtypes(include=['uint8']).columns.tolist()
-    num_encoding[uint_cols] = num_encoding[uint_cols].astype('int64')
-    stored_encoding = pd.read_csv(test_data_folder/'fake_test_sample_numerical_encoded.csv')
+    stored_encoding = pd.read_parquet(test_data_folder/'fake_test_sample_numerical_encoded.parquet')
     pd.testing.assert_frame_equal(num_encoding, stored_encoding)