Python 基于列中的值运行模拟
我已经编写了一些代码,根据一些条件模拟熊猫数据帧中的值。我现在只想对名为df['Use Type']的列中的特定值运行此代码。我目前有以下情况:Python 基于列中的值运行模拟,python,pandas,numpy,Python,Pandas,Numpy,我已经编写了一些代码,根据一些条件模拟熊猫数据帧中的值。我现在只想对名为df['Use Type']的列中的特定值运行此代码。我目前有以下情况: def l_sim(): n = 100 for i in range(n) df['RAND'] = np.random.uniform(0, 1, size=df.index.size) conditions = [df['RAND'] >= (1 - 0.8062), (df['RAND']
def l_sim():
n = 100
for i in range(n)
df['RAND'] = np.random.uniform(0, 1, size=df.index.size)
conditions = [df['RAND'] >= (1 - 0.8062), (df['RAND'] < (1 - 0.8062)) & (df['RAND'] >= 0.1),
(df['RAND'] < 0.1) & (df['RAND'] >= 0.05), (df['RAND'] < 0.05) &
(df['RAND'] >= 0.025), (df['RAND'] < 0.025) & (df['RAND'] >= 0.0125),
(df['RAND'] < 0.0125)]
choices = ['L0', 'L1', 'L2', 'L3', 'L4', 'L5']
df['L'] = np.select(conditions, choices)
conditions = [df['L'] == 'L0', df['L'] == 'L1', df['L'] == 'L2', df['L'] == 'L3',
df['L'] == 'L4', df['L'] == 'L5']
choices = [df['A'] * 0.02, df['A'] * 0.15, df['A'] * 0.20, df['A'] * 0.50,
df['A'] * 1, df['A'] * 1]
df['AL'] = np.select(conditions, choices)
l_sim()
我如何才能仅对具有df.loc[df['Use Type']=='Commercial Property']的行运行此代码
提前感谢。我认为您需要以不同的方式构建代码。但一般来说,可以使用df.apply和lambda函数。这种模式:
df['L'] = df.apply(lambda row: l_sim(row), axis=1)
我会将您的代码分成三个函数,一个用于df['L']:
第三种逻辑仅在行['Use Type']=='Commercial Property'时创建值:
要启动它:
df['L'] = df.apply(lambda row: l_sim(row), axis=1)
df['AL'] = df.apply(lambda row: l_sim(row), axis=1)
假设您的数据帧至少有两列“A”和“Use Type”,例如:
df = pd.DataFrame({'Use Type':['Commercial Property']*3+['other']*2, 'A':1})
然后通过以下方式修改函数:
def l_sim(df,use_type=None):
#check if you want to do it ont he whole datafrmae or a specific Use type
if use_type:
mask = df['Use Type'] == use_type
else:
mask = slice(None)
# generete the random values
df.loc[mask,'RAND'] = np.random.uniform(0, 1, size=df[mask].index.size)
# create conditions (same for both L and AL by the way)
conditions = [ df['RAND'] >= (1 - 0.8062), (df['RAND'] >= 0.1), (df['RAND'] >= 0.05),
(df['RAND'] >= 0.025), (df['RAND'] >= 0.0125), (df['RAND'] < 0.0125)]
#choices for the column L and create the column
choices_L = ['L0', 'L1', 'L2', 'L3', 'L4', 'L5']
df.loc[mask,'L'] = np.select(conditions, choices_L)[mask]
#choices for the column AL and create the column
choices_A = [df['A'] * 0.02, df['A'] * 0.15, df['A'] * 0.20, df['A'] * 0.50,
df['A'] * 1, df['A'] * 1]
df.loc[mask,'AL'] = np.select(conditions, choices_A)[mask]
及
我删除了的循环,因为我看不出重点,我简化了您的条件,就像前面问题中的一样为什么在代码中有循环?它似乎从未在您的代码中使用过do@Ben.T对于100范围内的每个I,我会得到一组不同的随机数,因此数据帧中的每一行都会有不同的“L”值。好的,但是如果在每个循环中重写同一列中的“L”值,那么上一个循环中的值就会被擦除。对于列AL也是一样,您的代码也会覆盖此列,而不考虑前面的循环
df['L'] = df.apply(lambda row: l_sim(row), axis=1)
df['AL'] = df.apply(lambda row: l_sim(row), axis=1)
df = pd.DataFrame({'Use Type':['Commercial Property']*3+['other']*2, 'A':1})
def l_sim(df,use_type=None):
#check if you want to do it ont he whole datafrmae or a specific Use type
if use_type:
mask = df['Use Type'] == use_type
else:
mask = slice(None)
# generete the random values
df.loc[mask,'RAND'] = np.random.uniform(0, 1, size=df[mask].index.size)
# create conditions (same for both L and AL by the way)
conditions = [ df['RAND'] >= (1 - 0.8062), (df['RAND'] >= 0.1), (df['RAND'] >= 0.05),
(df['RAND'] >= 0.025), (df['RAND'] >= 0.0125), (df['RAND'] < 0.0125)]
#choices for the column L and create the column
choices_L = ['L0', 'L1', 'L2', 'L3', 'L4', 'L5']
df.loc[mask,'L'] = np.select(conditions, choices_L)[mask]
#choices for the column AL and create the column
choices_A = [df['A'] * 0.02, df['A'] * 0.15, df['A'] * 0.20, df['A'] * 0.50,
df['A'] * 1, df['A'] * 1]
df.loc[mask,'AL'] = np.select(conditions, choices_A)[mask]
l_sim(df,'Commercial Property')
print (df)
Use Type A RAND L AL
0 Commercial Property 1 0.036593 L3 0.50
1 Commercial Property 1 0.114773 L1 0.15
2 Commercial Property 1 0.651873 L0 0.02
3 other 1 NaN NaN NaN
4 other 1 NaN NaN NaN
l_sim(df)
print (df)
Use Type A RAND L AL
0 Commercial Property 1 0.123265 L1 0.15
1 Commercial Property 1 0.906185 L0 0.02
2 Commercial Property 1 0.107588 L1 0.15
3 other 1 0.434560 L0 0.02
4 other 1 0.304901 L0 0.02