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Given the following text description, write Python code to implement the functionality described below step by step
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Description:
 seaborn.countplot
Bar graphs are useful for displaying relationships between categorical data and at least one numerical variable. seaborn.countplot is a barplot where the dependent variable is the number of instances of each instance of the independent variable.
dataset
Step1:  For the bar plot, let's look at the number of movies in each category, allowing each movie to be counted more than once.
Step2:  Basic plot
Step3:  color by a category
Step4:  make plot horizontal
Step5:  Saturation
Step6:  Targeting a non-default axes
Step7:  Add error bars
Step8:  add black bounding lines
Step9:  Remove color fill
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Python Code:
%matplotlib inline
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
plt.rcParams['figure.figsize'] = (20.0, 10.0)
plt.rcParams['font.family'] = "serif"
df = pd.read_csv('../../datasets/movie_metadata.csv')
df.head()
Explanation: seaborn.countplot
Bar graphs are useful for displaying relationships between categorical data and at least one numerical variable. seaborn.countplot is a barplot where the dependent variable is the number of instances of each instance of the independent variable.
dataset: IMDB 5000 Movie Dataset
End of explanation
# split each movie's genre list, then form a set from the unwrapped list of all genres
categories = set([s for genre_list in df.genres.unique() for s in genre_list.split("|")])
# one-hot encode each movie's classification
for cat in categories:
    df[cat] = df.genres.transform(lambda s: int(cat in s))
# drop other columns
df = df[['director_name','genres','duration'] + list(categories)]
df.head()
# convert from wide to long format and remove null classificaitons
df = pd.melt(df,
             id_vars=['duration'],
             value_vars = list(categories),
             var_name = 'Category',
             value_name = 'Count')
df = df.loc[df.Count>0]
top_categories = df.groupby('Category').aggregate(sum).sort_values('Count', ascending=False).index
howmany=10
# add an indicator whether a movie is short or long, split at 100 minutes runtime
df['islong'] = df.duration.transform(lambda x: int(x > 100))
df = df.loc[df.Category.isin(top_categories[:howmany])]
# sort in descending order
#df = df.loc[df.groupby('Category').transform(sum).sort_values('Count', ascending=False).index]
df.head()
Explanation: For the bar plot, let's look at the number of movies in each category, allowing each movie to be counted more than once.
End of explanation
p = sns.countplot(data=df, x = 'Category')
Explanation: Basic plot
End of explanation
p = sns.countplot(data=df,
                  x = 'Category',
                  hue = 'islong')
Explanation: color by a category
End of explanation
p = sns.countplot(data=df,
                  y = 'Category',
                  hue = 'islong')
Explanation: make plot horizontal
End of explanation
p = sns.countplot(data=df,
                  y = 'Category',
                  hue = 'islong',
                  saturation=1)
Explanation: Saturation
End of explanation
import matplotlib.pyplot as plt
fig, ax = plt.subplots(2)
sns.countplot(data=df,
                  y = 'Category',
                  hue = 'islong',
                  saturation=1,
                  ax=ax[1])
Explanation: Targeting a non-default axes
End of explanation
import numpy as np
num_categories = df.Category.unique().size
p = sns.countplot(data=df,
                  y = 'Category',
                  hue = 'islong',
                  saturation=1,
                  xerr=7*np.arange(num_categories))
Explanation: Add error bars
End of explanation
import numpy as np
num_categories = df.Category.unique().size
p = sns.countplot(data=df,
                  y = 'Category',
                  hue = 'islong',
                  saturation=1,
                  xerr=7*np.arange(num_categories),
                  edgecolor=(0,0,0),
                  linewidth=2)
Explanation: add black bounding lines
End of explanation
import numpy as np
num_categories = df.Category.unique().size
p = sns.countplot(data=df,
                  y = 'Category',
                  hue = 'islong',
                  saturation=1,
                  xerr=7*np.arange(num_categories),
                  edgecolor=(0,0,0),
                  linewidth=2,
                  fill=False)
import numpy as np
num_categories = df.Category.unique().size
p = sns.countplot(data=df,
                  y = 'Category',
                  hue = 'islong',
                  saturation=1,
                  xerr=7*np.arange(num_categories),
                  edgecolor=(0,0,0),
                  linewidth=2)
sns.set(font_scale=1.25)
num_categories = df.Category.unique().size
p = sns.countplot(data=df,
                  y = 'Category',
                  hue = 'islong',
                  saturation=1,
                  xerr=3*np.arange(num_categories),
                  edgecolor=(0,0,0),
                  linewidth=2)
help(sns.set)
plt.rcParams['font.family'] = "cursive"
#sns.set(style="white",font_scale=1.25)
num_categories = df.Category.unique().size
p = sns.countplot(data=df,
                  y = 'Category',
                  hue = 'islong',
                  saturation=1,
                  xerr=3*np.arange(num_categories),
                  edgecolor=(0,0,0),
                  linewidth=2)
plt.rcParams['font.family'] = 'Times New Roman'
#sns.set_style({'font.family': 'Helvetica'})
sns.set(style="white",font_scale=1.25)
num_categories = df.Category.unique().size
p = sns.countplot(data=df,
                  y = 'Category',
                  hue = 'islong',
                  saturation=1,
                  xerr=3*np.arange(num_categories),
                  edgecolor=(0,0,0),
                  linewidth=2)
bg_color = (0.25, 0.25, 0.25)
sns.set(rc={"font.style":"normal",
            "axes.facecolor":bg_color,
            "figure.facecolor":bg_color,
            "text.color":"black",
            "xtick.color":"black",
            "ytick.color":"black",
            "axes.labelcolor":"black"})
#sns.set_style({'font.family': 'Helvetica'})
#sns.set(style="white",font_scale=1.25)
num_categories = df.Category.unique().size
p = sns.countplot(data=df,
                  y = 'Category',
                  hue = 'islong',
                  saturation=1,
                  xerr=3*np.arange(num_categories),
                  edgecolor=(0,0,0),
                  linewidth=2,
                  palette="Dark2")
leg = p.get_legend()
leg.set_title("Duration")
labs = leg.texts
labs[0].set_text("Short")
labs[1].set_text("Long")
leg.get_title().set_color('white')
for lab in labs:
    lab.set_color('white')
p.axes.xaxis.label.set_text("Counts")
plt.text(900,0, "Count Plot", fontsize = 95, color='white', fontstyle='italic')
p.get_figure().savefig('../figures/countplot.png')
Explanation: Remove color fill
End of explanation
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