How to plot multiple bar charts with different infill and outline? I have the CSV's of data. I want to plot the distribution of every attribute or a single one to compare the distribution from other CSV's, Since all attributes are the same but the distribution is different from each other because I have made the synthetic data using the original one.
Just like shown below that one attribute distribution is different from the other, I don't know the name of this chart. How could I plot this :

Code I tried so far:
    data=pd.read_csv(r'/content/drive/MyDrive/census-income.data.csv',names=['age','class of worker','detailed industry recode','detailed occupation recode','education','wage per hour','enroll in edu inst last wk','martial status','major industry code','major occupation code','race','hispanic origin','sex','member of labour union','reason for unemployment','full or part time employment stat','capital gains','capital losses','divdends from stock','tax filer status','region of previous residence','state of previous residence','detailed household and family stat','detailed household summary in household','instance weight','migration code-change in msa','migration code-change in reg','migration code-move within reg','live in this house 1 year ago',' migration prev res in sunbelt','num persons worked for employer','family members under 18','country of birth father','country of birth mother','country of birth self','citizenship','own business or self employed','fill inc questionnaire for veterans admin','veterans benefits','weeks worked in year','year','income'])
    data=data.drop(['state of previous residence','migration code-change in msa','migration code-change in reg','migration code-move within reg',' migration prev res in sunbelt','country of birth father','country of birth mother','country of birth self','instance weight'],axis=1)
    s_data=pd.read_csv('/content/drive/MyDrive/SyntheticData/SDV/SDV_TRAIN_DATA.csv')
    d_data=pd.read_csv('/content/drive/MyDrive/SyntheticData/Data Synthesizer/DataSynthesizerCensusTrain.csv')
    sp_data=pd.read_csv('/content/drive/MyDrive/SyntheticData/Synthpop/CensusTrainData.csv')
    import matplotlib.pyplot as plt
    import numpy as np
    import pandas as pd
    
    # example data
    x = np.arange(len(data))
    
    fig, ax = plt.subplots(figsize=(50,48))
    
    ax.bar(data['age'], x,color=&quot;red&quot;,  edgecolor=&quot;None&quot;, linewidth=4, label='Original Data')
    ax.bar(s_data['age'],x,color=&quot;None&quot;, edgecolor=&quot;blue&quot;, linewidth=4, label='Synthetic Data Vault')
    # ax.bar(d_data['age'],x,color=&quot;None&quot;, edgecolor=&quot;green&quot;, linewidth=4, label='Data Synthesizer')
    # ax.bar(x, sp_data, color=&quot;None&quot;, edgecolor=&quot;green&quot;, linewidth=2, label='Synthpop')
    ax.legend()
    
    plt.show()
Use (more information in the docs):
import matplotlib.pyplot as plt
import numpy as np

# example data
x = list(range(14))
y1 = np.arange(14)
y2 = np.arange(14)[::-1]
y3 = [8 for x in range(14)]

# Interesting part
fig, ax = plt.subplots()

ax.bar(x, y1, color=&quot;red&quot;,  edgecolor=&quot;None&quot;, linewidth=2, label='1')
ax.bar(x, y2, color=&quot;None&quot;, edgecolor=&quot;blue&quot;, linewidth=2, label='2')
ax.bar(x, y3, color=&quot;None&quot;, edgecolor=&quot;green&quot;, linewidth=2, label='3')
ax.legend()

plt.show()