1.一般运算

• 加法

# gen x = y + z
df['x'] = df['y'] + df['z']

• 减法

# gen x = y - 1
df['x'] = df['y'] - 1

• 乘法

# gen var = x * y
df['var'] = df['x'] * df['y']

• 除法

# gen x = z / y
df['x'] = df['z'] / df['y']

• 取对数

# gen logx = log(x)
df['logx'] = np.log(df['x'])

• 开根号

# gen z = sqrt(y)
df['z'] = np.sqrt(df['y'])

• 取平方

# gen x2 = x^2
df['x2'] = df['x'] ** 2

• y 列对 3 取模

# gen x = mod(y,3)
df['x'] = df['y'] % 3

• 向上或向下取整

# gen x = floor(y)
df['x'] = np.floor(df['y'])# gen x = ceil(y)
df['x'] = np.ceil(df['y'])

2.对列进行处理

• 生成新变量

# gen x = 1 if (r2 == 0 | r2 == 1)
condition = (df['r2'] == 0) | (df['r2'] == 1)
df.loc[condition, 'x'] = 1# gen childage = age if r2 == 2
df.loc[df['r2'] == 2 , 'childage' ] = df['age']

• 删除变量（列）

# drop r7_1
df = df.drop(['r7_1'], axis = 1)
df = df.drop(['mx','x'],axis = 1)

3.对行进行处理

• 删除行

## 有条件的
# drop if childage < 18 | childage > 30
condition = (df['childage'] < 18) | (df['childage'] > 30)
df = df.drop(df[condition].index)
df = df.drop(df[(df['childage'] < 18) | (df['childage'] > 30)].index, axis=0)   # 等价## 删除缺失值
df = df.dropna(subset= ['mx'])

• 保留行

# keep if r2 <= 2
df = df[df['r2'] <= 2]

• 替换行与生成新变量类似

# replace hedu=2 if if childage < 18 | childage > 30
condition = (df['childage'] < 18) | (df['childage'] > 30)
df.loc[condition, 'hedu'] = 2# replace hedu=0 if hedu==.   // 把缺失值替换为0
df['hedu'] = df['hedu'].fillna(0)

4.分组计算

# bysort h1: egen mx=mean(x)
df['mx'] = df.groupby('h1')['x'].transform('mean')
df['mx'].value_counts()# bysort h1 : egen htype = total(x)
df['htype'] = df.groupby('h1')['x'].transform('sum')# bysort h1: egen htype=count(id)
df['htype'] = df.groupby('h1')['id'].transform('count')

常见 transform函数

• sum：对每个分组计算总和
• mean：对每个分组计算均值
• count：对每个分组计算非空值的数量
• size：对每个分组计算总行数（包括空值）
• min：对每个分组计算最小值
• max：对每个分组计算最大值
• std：对每个分组计算标准差
• var：对每个分组计算方差
• first：返回每个分组的第一个值
• last：返回每个分组的最后一个值
• median：对每个分组计算中位数

还可以传递自定义的函数到 transform() 中，例如使用 lambda 表达式：

df['double_x'] = df.groupby('h1')['x'].transform(lambda x: x * 2)