In [1]:

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

sns.set(style="darkgrid", color_codes=True)

In [2]:

df = pd.read_csv('Pokemon.csv', index_col=0, encoding='latin1')
df.head()

Out[2]:

	Name	Type 1	Type 2	Total	HP	Attack	Defense	Sp. Atk	Sp. Def	Speed	Stage	Legendary
#
1	Bulbasaur	Grass	Poison	318	45	49	49	65	65	45	1	False
2	Ivysaur	Grass	Poison	405	60	62	63	80	80	60	2	False
3	Venusaur	Grass	Poison	525	80	82	83	100	100	80	3	False
4	Charmander	Fire	NaN	309	39	52	43	60	50	65	1	False
5	Charmeleon	Fire	NaN	405	58	64	58	80	65	80	2	False

In [3]:

df['Stage'].value_counts()

Out[3]:

1    79
2    56
3    16
Name: Stage, dtype: int64

In [4]:

# scatter plot

# using FacetGrid

g = sns.FacetGrid(df, hue="Stage", size=5) \
   .map(plt.scatter, "Attack", "Defense") \
   .add_legend()

In [5]:

f, ax = plt.subplots(nrows=1, ncols=3, sharey=True, figsize=(15, 5))

ax[0].set_title("Stage 1")
ax[0].scatter(df.loc[df['Stage'] == 1, 'Attack'], df.loc[df['Stage'] == 1, 'Defense'], c="b")

ax[1].set_title("Stage 2")
ax[1].scatter(df.loc[df['Stage'] == 2, 'Attack'], df.loc[df['Stage'] == 2, 'Defense'], c="#FF8C00")

ax[2].set_title("Stage 3")
ax[2].scatter(df.loc[df['Stage'] == 3, 'Attack'], df.loc[df['Stage'] == 3, 'Defense'], c="g")
plt.show()

In [ ]:

In [6]:

# Using lmplot

	
# Plot using Seaborn
sns.lmplot(x='Attack', y='Defense', data=df,
           fit_reg=False, 
           hue='Stage')
 
# Tweak using Matplotlib
plt.ylim(0, None)
plt.xlim(0, None)

Out[6]:

(0, 142.0200544906389)

In [7]:

# Default boxplotPython
# Boxplot
g = sns.boxplot(data=df)

In [8]:

# Pre-format DataFrame
stats_df = df.drop(['Total', 'Stage', 'Legendary'], axis=1)
 
# New boxplot using stats_df
g = sns.boxplot(data=stats_df)

In [9]:

# Set theme
sns.set_style('whitegrid')

plt.figure(figsize=(15, 8))
# Violin plot

g = sns.violinplot(x='Type 1', y='Attack', data=df)

In [10]:

pkmn_type_colors = ['#78C850',  # Grass
                    '#F08030',  # Fire
                    '#6890F0',  # Water
                    '#A8B820',  # Bug
                    '#A8A878',  # Normal
                    '#A040A0',  # Poison
                    '#F8D030',  # Electric
                    '#E0C068',  # Ground
                    '#EE99AC',  # Fairy
                    '#C03028',  # Fighting
                    '#F85888',  # Psychic
                    '#B8A038',  # Rock
                    '#705898',  # Ghost
                    '#98D8D8',  # Ice
                    '#7038F8',  # Dragon
                   ]

In [11]:

plt.figure(figsize=(15, 8))

# Violin plot with Pokemon color palette
g = sns.violinplot(x='Type 1', y='Attack', data=df, 
               palette=pkmn_type_colors) # Set color palette

In [12]:

plt.figure(figsize=(15, 8))

g = sns.swarmplot(x='Type 1', y='Attack', data=df, 
              palette=pkmn_type_colors)

In [13]:

plt.figure(figsize=(15, 8))

# Create plot
sns.violinplot(x='Type 1',
               y='Attack', 
               data=df, 
               inner=None, # Remove the bars inside the violins
               palette=pkmn_type_colors)
 
sns.swarmplot(x='Type 1', 
              y='Attack', 
              data=df, 
              color='k', # Make points black
              alpha=0.7) # and slightly transparent
 
# Set title with matplotlib
plt.title('Attack by Type')
plt.show()

In [14]:

# Putting it all together

stats_df.head()

Out[14]:

	Name	Type 1	Type 2	HP	Attack	Defense	Sp. Atk	Sp. Def	Speed
#
1	Bulbasaur	Grass	Poison	45	49	49	65	65	45
2	Ivysaur	Grass	Poison	60	62	63	80	80	60
3	Venusaur	Grass	Poison	80	82	83	100	100	80
4	Charmander	Fire	NaN	39	52	43	60	50	65
5	Charmeleon	Fire	NaN	58	64	58	80	65	80

In [15]:

# https://www.journaldev.com/33398/pandas-melt-unmelt-pivot-function
# https://www.geeksforgeeks.org/python-read-blob-object-in-python-using-wand-library/?ref=rp

melted_df = pd.melt(stats_df,
id_vars=["Name", "Type 1", "Type 2"],
var_name="Stat")
melted_df.head()

Out[15]:

	Name	Type 1	Type 2	Stat	value
0	Bulbasaur	Grass	Poison	HP	45
1	Ivysaur	Grass	Poison	HP	60
2	Venusaur	Grass	Poison	HP	80
3	Charmander	Fire	NaN	HP	39
4	Charmeleon	Fire	NaN	HP	58

In [16]:

print( stats_df.shape )
print( melted_df.shape )

(151, 9)
(906, 5)

In [17]:

plt.figure(figsize=(20, 8))

# Swarmplot with melted_df

g = sns.swarmplot(x="Stat", y="value", hue="Type 1", data=melted_df)
plt.legend(bbox_to_anchor=(1, 1), loc=2)

Out[17]:

<matplotlib.legend.Legend at 0x1c905044f48>

In [18]:

plt.figure(figsize=(20, 8))

sns.swarmplot(x='Stat', 
              y='value', 
              data=melted_df, 
              hue='Type 1', 
              split=True, # 2. Separate points by hue
              palette=pkmn_type_colors) # 3. Use Pokemon palette
 
# 4. Adjust the y-axis
plt.ylim(0, 260)
 
# 5. Place legend to the right
plt.legend(bbox_to_anchor=(1, 1), loc=2)

Out[18]:

<matplotlib.legend.Legend at 0x1c90550e108>

In [19]:

# Heatmaps

# Calculate correlations
corr = stats_df.corr()
 
# Heatmap
g = sns.heatmap(corr)

In [20]:

# Histogram

# Distribution Plot (a.k.a. Histogram)
sns.distplot(df.Attack)

Out[20]:

<matplotlib.axes._subplots.AxesSubplot at 0x1c904e8a5c8>

In [21]:

# bar plot

# Count Plot (a.k.a. Bar Plot)
sns.countplot(x='Type 1', data=df, palette=pkmn_type_colors)
 
# Rotate x-labels
plt.xticks(rotation=-45)

Out[21]:

(array([ 0,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12, 13, 14]),
 <a list of 15 Text xticklabel objects>)

In [22]:

# Factor Plot - Factor plots make it easy to separate plots by categorical classes.

g = sns.factorplot(x='Type 1', 
                   y='Attack', 
                   data=df, 
                   hue='Stage',  # Color by stage
                   col='Stage',  # Separate by stage
                   kind='swarm') # Swarmplot
 
# Rotate x-axis labels
g.set_xticklabels(rotation=-45)

Out[22]:

<seaborn.axisgrid.FacetGrid at 0x1c9049ea348>

In [23]:

# Joint Distribution Plot
sns.jointplot(x='Attack', y='Defense', kind="kde", data=df)

Out[23]:

<seaborn.axisgrid.JointGrid at 0x1c904ea1f88>

In [24]:

# Density Plot
sns.kdeplot(df.Attack, df.Defense)

Out[24]:

<matplotlib.axes._subplots.AxesSubplot at 0x1c904dca7c8>