第十天：Python数据分析：从0完成一个宝可梦数据分析实战（第10天）

口袋妖怪数据集探索

数据集下载

!wget -O pokemon_data.csv https://pai-public-data.oss-cn-beijing.aliyuncs.com/pokemon/pokemon.csv

import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt
df = pd.read_csv("./pokemon_data.csv")
df.head()

查看数据

# 计算出每个特征有多少百分比是缺失的
percent_missing = df.isnull().sum() * 100 / len(df)
missing_value_df = pd.DataFrame({
     
    'column_name': df.columns,
    'percent_missing': percent_missing
})
# 查看Top10缺失的
missing_value_df.sort_values(by='percent_missing', ascending=False).head(10)
# 计算出每个特征有多少百分比是缺失的
percent_missing = df.isnull().sum() * 100 / len(df)
missing_value_df = pd.DataFrame({
     
    'column_name': df.columns,
    'percent_missing': percent_missing
})
missing_value_df.sort_values(by='percent_missing', ascending=False).head(10)

查看各代口袋妖怪的数量

df['generation'].value_counts().sort_values(ascending=False).plot.bar()

# 查看每个系口袋妖怪的数量
df['type1'].value_counts().sort_values(ascending=True).plot.barh()

# 相关性热力图分析
plt.subplots(figsize=(20,15))
ax = plt.axes()
ax.set_title("Correlation Heatmap")
corr = df.corr()
sns.heatmap(corr, 
            xticklabels=corr.columns.values,
            yticklabels=corr.columns.values)

interested = ['hp','attack','defense','sp_attack','sp_defense','speed']
sns.pairplot(df[interested])

# 通过相关性分析heatmap分析五个基础属性
plt.subplots(figsize=(10,8))
ax = plt.axes()
ax.set_title("Correlation Heatmap")
corr = df[interested].corr()
sns.heatmap(corr, 
            xticklabels=corr.columns.values,
            yticklabels=corr.columns.values,
            annot=True, fmt="f",cmap="YlGnBu")

for c in interested:
    df[c] = df[c].astype(float)
df = df.assign(total_stats = df[interested].sum(axis=1)) 
df[df.total_stats >= 525].shape
# 种族值分布
total_stats = df.total_stats
plt.hist(total_stats,bins=35)
plt.xlabel('total_stats')
plt.ylabel('Frequency')