Init code for demographic data

- Put into dataframe
 - Plot values of each column
 - Only age that is numerical (float) data. Other than data is object type
 - Data has been preprocessed from original raw data (*.csv) so that it is easy to processed

Demographic/demographic.py

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+# ---
+# jupyter:
+#   jupytext:
+#     cell_metadata_filter: -all
+#     custom_cell_magics: kql
+#     text_representation:
+#       extension: .py
+#       format_name: percent
+#       format_version: '1.3'
+#       jupytext_version: 1.11.2
+#   kernelspec:
+#     display_name: Python 3.8.10 ('hyperscanning2_redesign_new')
+#     language: python
+#     name: python3
+# ---
+
+# %% [markdown]
+# ## Relevant packages
+
+# %%
+import pandas as pd
+
+# %% [markdown]
+# ## Load the file
+
+# %%
+df = pd.read_csv("/hpc/igum002/codes/Hyperscanning2-redesign/data/Demographic/demographic_exp2_redesign.csv")
+
+# Get required columns
+start_idx = df.columns.get_loc("Participant ID")
+df = df.iloc[ 2:, start_idx : ]
+
+# Convert object type to numeric (for age)
+df["Age"] = pd.to_numeric(df.Age, errors="coerce")
+
+# Reset index to zero
+df = df.reset_index(drop=True)
+df.head()
+
+# %% [markdown]
+# ## Plots
+
+# %% [markdown]
+# ### Gender
+
+# %%
+df["Gender"].value_counts().plot(kind="bar", xlabel="Gender", ylabel="Count", title="Number of participants by Gender");
+
+# %% [markdown]
+# ### Counts of gender
+
+# %%
+df["Gender"].value_counts()
+
+# %% [markdown]
+# ### Plot Age
+
+# %%
+df.hist(bins=10);
+
+# %% [markdown]
+# ### Statistics of age
+
+# %%
+df["Age"].describe()
+
+# %% [markdown]
+# ### Marital status
+#
+
+# %%
+df["Marital Status"].value_counts().plot(kind="bar", xlabel="Marital Status", ylabel="Count", title="Number of participants by marital status");
+
+# %% [markdown]
+# ### Counts of marital status
+
+# %%
+df["Marital Status"].value_counts()
+
+# %% [markdown]
+# ### Ethnicity
+
+# %%
+df["Ethnicity"].value_counts().plot(kind="bar", xlabel="Ethnicity", ylabel="Count", title="Number of participants by Ethnicity");
+
+# %% [markdown]
+# ### Counts of ethnicity
+
+# %%
+df["Ethnicity"].value_counts()
+
+# %% [markdown]
+# ### Employment
+
+# %%
+df["Employment"].value_counts().plot(kind="bar", xlabel="Employment", ylabel="Count", title="Number of participants by Employment");
+
+# %% [markdown]
+# ### Counts of employment type
+
+# %%
+df["Employment"].value_counts()
+
+# %% [markdown]
+# ### Hand dominant
+
+# %%
+df["Hand Dominant"].value_counts().plot(kind="bar", xlabel="Hand Dominant", ylabel="Count", title="Number of participants by Hand Dominant");
+
+# %% [markdown]
+# ### Counts of hand dominant
+
+# %%
+df["Hand Dominant"].value_counts()
+
+# %% [markdown]
+# ### Eye Dominant
+
+# %%
+df["Eye Dominant"].value_counts().plot(kind="bar", xlabel="Eye Dominant", ylabel="Count", title="Number of participants by Eye Dominant");
+
+# %% [markdown]
+# ### Counts of eye dominant
+
+# %%
+df["Eye Dominant"].value_counts()
+
+# %% [markdown]
+# ### Foot/Leg Dominant
+
+# %%
+df["Foot/Leg Dominant"].value_counts().plot(kind="bar", xlabel="Foot Dominant", ylabel="Count", title="Number of participants by Foot Dominant");
+
+# %% [markdown]
+# ### Counts of foot dominant
+
+# %%
+df["Foot/Leg Dominant"].value_counts()
+
+# %% [markdown]
+# ### VR Experience
+
+# %%
+df["VR Experience"].value_counts().plot(kind="bar", xlabel="VR Experience", ylabel="Count", title="Number of participants by VR Experience");
+
+# %% [markdown]
+# ### Counts of VR experience
+
+# %%
+df["VR Experience"].value_counts()
+
+# %% [markdown]
+# ### Frequency of using VR
+
+# %%
+df["Freq. of using VR"].value_counts().plot(kind="bar", xlabel="Freq. of using VR", ylabel="Count", title="Number of participants by Freq of using VR");
+
+# %% [markdown]
+# ### Counts of frequency of using VR
+
+# %%
+df["Freq. of using VR"].value_counts()