histogram, lineplot and countplot in Python

import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns

#Lineplot for every row
transposed_df_liabilities = balance_df_liabilities.T
transposed_df_liabilities_percentage = transposed_df_liabilities.div(transposed_df_liabilities.sum(axis=1).replace(0, 1), axis=0) * 100
transposed_df_liabilities_percentage

transposed_df_liabilities.plot(subplots= True, figsize=(10,12), sharex= False, sharey=False)
# OR in percentage
ax = transposed_df_liabilities_percentage.plot(kind='line', subplots=True,figsize=(10,12), sharex= False, sharey=False, layout=(len(transposed_df_liabilities_percentage.columns), 1), legend=True)
for subplot in ax:
    for a in subplot:
        a.yaxis.set_major_formatter(mtick.PercentFormatter(xmax =100))
plt.tight_layout()  # Adjusts plot spacing
plt.show()


#Histograms with two different columns
plt.figure(figsize = (20,25))
sns.set(font_scale=2.5)
fig, ax = plt.subplots()
ax.bar(month_quantity_2017['TransactionDate'], month_quantity_2017['Quantity'])

# How to set the size
sns.set(font_scale=8.7)
fig, ax = plt.subplots(figsize=(80, 40))
ax.bar(category_quantity_2017['ACM Application L2 Ver2'], category_quantity_2017['Quantity'])
plt.xlabel('Product Category')
plt.xticks(rotation=90)
plt.ylabel('Quantity')
plt.title('Sold Quantity from each category (ACM Application L2 Ver2) in 2017')
plt.show()

#########################################################
df_diesel["bins"] = pd.cut(df_diesel['Price BGN'], [0,5000,10000,15000,20000,2000000])
ax = df_diesel["bins"].value_counts(normalize=True).plot.bar()
ax.bar_label(ax.containers[0], label_type='edge')
plt.xlabel('Price BGN')
plt.ylabel('Percentages')
plt.title("Diesel engines: Price Distribution")
plt.show()

#####################################################
# Count plot
plt.figure(figsize=(12,8))
df_actors_last.Actor.value_counts().head(20).plot(kind = 'bar', fontsize = 15)
plt.show()

df.sort_values(by=['age'])
plt.figure(figsize=(20,15), dpi = 200)
sns.countplot(data = df.sort_values(by=['age']), x = 'age', palette = 'magma' )
plt.show()

plt.figure(figsize=(12,8))
sns.set(font_scale=1.1, palette="viridis")
sns.countplot(data = listings.loc[listings.IPO_Year > 2000], x = "IPO_Year", hue = "Exchange")
plt.legend(loc= "upper left")
plt.savefig("list_countpl")

####################################################
sns.set(rc={'figure.figsize':(10.7,8.7)})
plt.xticks(rotation=90)
sns.set(style='darkgrid')
plt.figure(figsize=(10,5), dpi = 200)
sns.histplot(data=df, x = 'age', color = 'blue', linewidth = 2)
plt.show()

#####################################################
plt.style.available
plt.style.use("seaborn) # how to choose the style

######################################################
titanic.age.hist(figsize(12,8), bins=80, xlabelsize=15, ylabelsize=15)
plt.show()

######################################################
#  Create histogram
sns.set(style='darkgrid')
plt.figure(figsize=(5,8), dpi = 200)
distribution = sns.histplot(data=df, x = 'salary', bins = 10, color = 'red', edgecolor = 'blue', linewidth = 4, kde = True)
print(distribution)

################################################## How to plot value counts
plt.figure(figsize=(20,12))
fandago['YEAR'].value_counts().plot(kind='barh')
plt.show()

##################################################
plt.figure(figsize=(20,15), dpi = 200)
sns.countplot(data = fandago, x = 'STARS_DIFF', palette = 'magma' )
plt.title('Number of times a difference occurs')
plt.show()

##################################################
xticks = [x for x in range(0,901, 50)]
yticks = [y for y in range(0, 81, 5)]
plt.style.use("classic")

titanic.age.plot(figsize = (12,8), fontsize= 13, c = "r", linestyle = "-",
                 xlim = (0,900), ylim = (0,80), xticks = xticks, yticks = yticks, rot = 45)
plt.title("Titanic - Ages", fontsize = 15)
plt.legend(loc = 3, fontsize = 15)
plt.xlabel("Passenger No", fontsize = 13)
plt.ylabel("Age", fontsize = 13)
#plt.grid()
plt.show()

#########################################################
plt.style.use("ggplot")
plt.figure(figsize=(12, 8))
plt.bar(df_converted['Year'], df_converted['Dividends'], color = 'b' )
plt.xlabel('Year')
plt.ylabel('Total Dividends')
plt.title('Dividends by Year')
plt.show()


plt.figure(figsize=(12,8))
sns.set(font_scale=1.0)
sns.barplot(data = listings , x = 'Exchange', y = "Market_Cap", dodge = True)
plt.show()