Merge pull request #98 from UBC-MDS/viz-update

Update visualise.py to include display argument

src/datexplore/visualise.py

@@ -2,12 +2,12 @@
 import seaborn as sns
 import matplotlib.pyplot as plt
 
-def visualise(df):
+def visualise(df, display = False):
     """This function generates visualizations for a pandas DataFrame to identify patterns in missing values, correlation between variables, and distribution of variables and variable pairs.
 
     This function creates three types of plots:
     1. A heatmap of missing values: Each cell in the heatmap represents a value in the DataFrame. Cells are colored differently to indicate whether the value is missing or not. 
-    This helps in identifying patterns or areas with missing data.
+This helps in identifying patterns or areas with missing data.
     2. A correlation heatmap: This heatmap shows the correlation coefficients between all pairs of columns in the DataFrame.
        High positive or negative values indicate strong relationships, while values close to zero suggest weak relationship. This is useful for understanding the relationships between variables.
     3. A pairplot: This creates a grid of scatter plots for each pair of variables in the DataFrame. It helps in visualizing the distribution of individual variables and the relationships between them.
@@ -47,7 +47,7 @@ def visualise(df):
         plt.title('Correlation Heatmap of Variables in DataFrame')
         if display==True:
             plt.show()
-
+            
         sns.pairplot(df)
         plt.figure(figsize=(10, 4))
         if display==True: