31 Pandas使用explode实现一行变多行统计
解决实际问题：一个字段包含多个值，怎样将这个值拆分成多行，然后实现统计 比如：一个电影有多个分类、一个人有多个喜好，需要按分类、喜好做统计
1、读取数据
import pandas as pd
df = pd.read_csv(
"./datas/movielens-1m/movies.dat",
header=None,
names="MovieID::Title::Genres".split("::"),
sep="::",
engine="python"
)
df.head()
.dataframe tbody tr th:only-of-type {
vertical-align: middle;
}
<pre><code>.dataframe tbody tr th {
vertical-align: top;
}
.dataframe thead th {
text-align: right;
}
</code></pre>
| MovieID | Title | Genres | |
|---|---|---|---|
| 0 | 1 | Toy Story (1995) | Animation|Children's|Comedy |
| 1 | 2 | Jumanji (1995) | Adventure|Children's|Fantasy |
| 2 | 3 | Grumpier Old Men (1995) | Comedy|Romance |
| 3 | 4 | Waiting to Exhale (1995) | Comedy|Drama |
| 4 | 5 | Father of the Bride Part II (1995) | Comedy |
问题：怎样实现这样的统计，每个题材有多少部电影？
解决思路：
- 将Genres按照分隔符|拆分
- 按Genres拆分成多行
- 统计每个Genres下的电影数目
2、将Genres字段拆分成列表
df.info()
<class 'pandas.core.frame.DataFrame'>
RangeIndex: 3883 entries, 0 to 3882
Data columns (total 3 columns):
MovieID 3883 non-null int64
Title 3883 non-null object
Genres 3883 non-null object
dtypes: int64(1), object(2)
memory usage: 91.1+ KB
# 当前的Genres字段是字符串类型
type(df.iloc[0]["Genres"])
str
# 新增一列
df["Genre"] = df["Genres"].map(lambda x:x.split("|"))
df.head()
.dataframe tbody tr th:only-of-type {
vertical-align: middle;
}
<pre><code>.dataframe tbody tr th {
vertical-align: top;
}
.dataframe thead th {
text-align: right;
}
</code></pre>
| MovieID | Title | Genres | Genre | |
|---|---|---|---|---|
| 0 | 1 | Toy Story (1995) | Animation|Children's|Comedy | [Animation, Children's, Comedy] |
| 1 | 2 | Jumanji (1995) | Adventure|Children's|Fantasy | [Adventure, Children's, Fantasy] |
| 2 | 3 | Grumpier Old Men (1995) | Comedy|Romance | [Comedy, Romance] |
| 3 | 4 | Waiting to Exhale (1995) | Comedy|Drama | [Comedy, Drama] |
| 4 | 5 | Father of the Bride Part II (1995) | Comedy | [Comedy] |
# Genre的类型是列表
print(df["Genre"][0])
print(type(df["Genre"][0]))
['Animation', "Children's", 'Comedy']
<class 'list'>
df.info()
<class 'pandas.core.frame.DataFrame'>
RangeIndex: 3883 entries, 0 to 3882
Data columns (total 4 columns):
MovieID 3883 non-null int64
Title 3883 non-null object
Genres 3883 non-null object
Genre 3883 non-null object
dtypes: int64(1), object(3)
memory usage: 121.5+ KB
3、使用explode将一行拆分成多行
语法：pandas.DataFrame.explode(column) 将dataframe的一个list-like的元素按行复制，index索引随之复制
df_new = df.explode("Genre")
df_new.head(10)
.dataframe tbody tr th:only-of-type {
vertical-align: middle;
}
<pre><code>.dataframe tbody tr th {
vertical-align: top;
}
.dataframe thead th {
text-align: right;
}
</code></pre>
| MovieID | Title | Genres | Genre | |
|---|---|---|---|---|
| 0 | 1 | Toy Story (1995) | Animation|Children's|Comedy | Animation |
| 0 | 1 | Toy Story (1995) | Animation|Children's|Comedy | Children's |
| 0 | 1 | Toy Story (1995) | Animation|Children's|Comedy | Comedy |
| 1 | 2 | Jumanji (1995) | Adventure|Children's|Fantasy | Adventure |
| 1 | 2 | Jumanji (1995) | Adventure|Children's|Fantasy | Children's |
| 1 | 2 | Jumanji (1995) | Adventure|Children's|Fantasy | Fantasy |
| 2 | 3 | Grumpier Old Men (1995) | Comedy|Romance | Comedy |
| 2 | 3 | Grumpier Old Men (1995) | Comedy|Romance | Romance |
| 3 | 4 | Waiting to Exhale (1995) | Comedy|Drama | Comedy |
| 3 | 4 | Waiting to Exhale (1995) | Comedy|Drama | Drama |
4、实现拆分后的题材的统计
%matplotlib inline
df_new["Genre"].value_counts().plot.bar()
<matplotlib.axes._subplots.AxesSubplot at 0x23d73917cc8>
本文使用 文章同步助手 同步
