pandas 将条件与上一行进行比较 [英] Pandas compare value with previous row with filtration condition

问题描述

我有一个DataFrame，其中包含有关员工薪水的信息。大约有900000行以上。

I have a DataFrame with information about employee salary. It's about 900000+ rows.

示例：

+----+-------------+---------------+----------+
|    |   table_num | name          |   salary |
|----+-------------+---------------+----------|
|  0 |      001234 | John Johnson  |     1200 |
|  1 |      001234 | John Johnson  |     1000 |
|  2 |      001235 | John Johnson  |     1000 |
|  3 |      001235 | John Johnson  |     1200 |
|  4 |      001235 | John Johnson  |     1000 |
|  5 |      001235 | Steve Stevens |     1000 |
|  6 |      001236 | Steve Stevens |     1200 |
|  7 |      001236 | Steve Stevens |     1200 |
|  8 |      001236 | Steve Stevens |     1200 |
+----+-------------+---------------+----------+

dtypes：

table_num: string
name: string
salary: float

我需要添加一列有关薪水水平下降的信息。
我正在使用 shift（）函数比较行中的值。

I need to add a column with information about increased\decreased salary level. I'm using the shift() function to compare value in rows.

主要问题是整个数据集中所有唯一员工的过滤和迭代。

Main problem is in filtration and iteration over all unique employees over the whole dataset.

我的脚本大约需要3个半小时。

如何更快地完成操作？

我的脚本：

# giving us only unique combination of 'table_num' and 'name'
    # since there can be same 'table_num' for different 'name'
    # and same names with different 'table_num' appears sometimes

names_df = df[['table_num', 'name']].drop_duplicates()

# then extracting particular name and table_num from Series
for i in range(len(names_df)):    ### Bottleneck of whole script ###    
    t = names_df.iloc[i,[0,1]][0]
    n = names_df.iloc[i,[0,1]][1]

    # using shift() and lambda to check if there difference between two rows 
    diff_sal = (df[(df['table_num']==t)
               & ((df['name']==n))]['salary'] - df[(df['table_num']==t)
                                                 & ((df['name']==n))]['salary'].shift(1)).apply(lambda x: 1 if x>0 else (-1 if x<0 else 0))
    df.loc[diff_sal.index, 'inc'] = diff_sal.values

样本输入数据：

df = pd.DataFrame({'table_num': ['001234','001234','001235','001235','001235','001235','001236','001236','001236'], 
                     'name': ['John Johnson','John Johnson','John Johnson','John Johnson','John Johnson', 'Steve Stevens', 'Steve Stevens', 'Steve Stevens', 'Steve Stevens'], 
                     'salary':[1200.,1000.,1000.,1200.,1000.,1000.,1200.,1200.,1200.]})

样本输出：

+----+-------------+---------------+----------+-------+
|    |   table_num | name          |   salary |   inc |
|----+-------------+---------------+----------+-------|
|  0 |      001234 | John Johnson  |     1200 |     0 |
|  1 |      001234 | John Johnson  |     1000 |    -1 |
|  2 |      001235 | John Johnson  |     1000 |     0 |
|  3 |      001235 | John Johnson  |     1200 |     1 |
|  4 |      001235 | John Johnson  |     1000 |    -1 |
|  5 |      001235 | Steve Stevens |     1000 |     0 |
|  6 |      001236 | Steve Stevens |     1200 |     0 |
|  7 |      001236 | Steve Stevens |     1200 |     0 |
|  8 |      001236 | Steve Stevens |     1200 |     0 |
+----+-------------+---------------+----------+-------+

推荐答案

使用< a href = https://pandas.pydata.org/pandas-docs/stable/generated/pandas.DataFrame.groupby.html rel = noreferrer> groupby 与 diff ：

df['inc'] = df.groupby(['table_num', 'name'])['salary'].diff().fillna(0.0)
df.loc[df['inc'] > 0.0, 'inc'] = 1.0
df.loc[df['inc'] < 0.0, 'inc'] = -1.0

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