.isna().sum(axis=0)
时间: 2024-05-31 21:10:08 浏览: 9
这是一个 pandas 库中的 DataFrame 对象的方法,用于计算每列缺失值的数量。其中,isna() 方法用于返回一个与原 DataFrame 相同大小的 Boolean 类型的 DataFrame,其中 True 表示缺失值,False 表示非缺失值。sum(axis=0) 方法用于对每列进行求和,即返回一个 Series 对象,其中每个元素表示对应列的缺失值数量。
相关问题
代码# 决策树分类模型 from sklearn.tree import DecisionTreeClassifier model_dt1 = DecisionTreeClassifier(max_leaf_nodes=16, random_state=123).fit(x_train.drop(['label'], axis=1), x_train['label'])报错ValueError: Input contains NaN, infinity or a value too large for dtype('float32').
这个错误提示意味着你的数据中包含了 NaN 值、无穷值或超出了 float32 类型的范围。你需要检查你的数据集,看看是否有这些问题。你可以使用 Pandas 库中的 isna() 函数找到 NaN 值,使用 NumPy 库中的 isinf() 函数找到无穷值。你可以使用以下代码来处理 NaN 和无穷值:
```python
import numpy as np
import pandas as pd
# 查找 NaN 值
print(df.isna().sum())
# 查找无穷值
print(np.isinf(df).sum())
# 填充 NaN 值
df.fillna(0, inplace=True)
# 替换无穷值
df = df.replace([np.inf, -np.inf], np.nan)
df.fillna(0, inplace=True)
```
同时,你也需要确保你的数据集中所有的列都是数值型的,如果不是,你需要进行数据类型转换。如果你的数据集中包含了无法转换为浮点数的数据,那么你需要先进行数据清洗。
Make them into functions - define parameter & return types etc. Resolve all errors flagged Adhere to PEP 8 coding style
Sure, here is the updated code with functions defined with parameter and return types and PEP8 coding style:
```python
import pandas as pd
import numpy as np
def calculate_IIRS_scores(df: pd.DataFrame) -> pd.DataFrame:
"""
Calculate IIRS scores and subscales.
Parameters:
- df (pd.DataFrame): DataFrame containing the data.
Returns:
- pd.DataFrame: DataFrame with calculated scores and subscales.
"""
if 'IIRS1' in df.columns:
try:
df['Nomiss_IIRSTotal'] = df[['IIRS1', 'IIRS2', 'IIRS3', 'IIRS4', 'IIRS5', 'IIRS6', 'IIRS7', 'IIRS8', 'IIRS9', 'IIRS10', 'IIRS11', 'IIRS12', 'IIRS13']].notna().sum(axis=1)
df['IIRSTotalScore'] = np.where(df['Nomiss_IIRSTotal'] >= 0.66*13, df[['IIRS1', 'IIRS2', 'IIRS3', 'IIRS4', 'IIRS5', 'IIRS6', 'IIRS7', 'IIRS8', 'IIRS9', 'IIRS10', 'IIRS11', 'IIRS12', 'IIRS13']].sum(axis=1), np.nan)
df.loc[df['IIRSTotalScore'] < 13, 'IIRSTotalScore'] = np.nan
df.rename(columns={'IIRSTotalScore': 'Summation (IIRS1 - IIRS13)'}, inplace=True)
df.drop('Nomiss_IIRSTotal', axis=1, inplace=True)
except KeyError:
pass
try:
df['Nomiss_IIRS_IntimacyTotal'] = df[['IIRS7', 'IIRS8']].notna().sum(axis=1)
df['IIRS_Intimacy'] = df[['IIRS7', 'IIRS8']].mean(axis=1).where(~df[['IIRS7', 'IIRS8']].isna().any(axis=1))
df.rename(columns={'IIRS_Intimacy': 'Intamacy Subscale Avg(IIRS7 & IIRS8)'}, inplace=True)
except KeyError:
pass
try:
df['IIRS_subscale1'] = df[['IIRS1', 'IIRS2']].mean(axis=1).where(~df[['IIRS1', 'IIRS2']].isna().any(axis=1))
df.rename(columns={'IIRS_subscale1': 'Physical Well-Being and Diet IIRS 1 & 2 - (IIRS1 + IIRS2)/2'}, inplace=True)
except KeyError:
pass
try:
df['IIRS_subscale2'] = df[['IIRS3', 'IIRS6']].mean(axis=1).where(~df[['IIRS3', 'IIRS6']].isna().any(axis=1))
df.rename(columns={'IIRS_subscale2': 'Work and Finances IIRS 3 & 6 - (IIRS3 + IIRS6)/2'}, inplace=True)
except KeyError:
pass
try:
df['IIRS_subscale3'] = df[['IIRS7', 'IIRS8', 'IIRS9']].mean(axis=1).where(~df[['IIRS7', 'IIRS8', 'IIRS9']].isna().any(axis=1))
df.rename(columns={'IIRS_subscale3': 'Marital, Sexual, and Family Relations IIRS 789 - (IIRS7 + IIRS8 + IIRS9)/3'}, inplace=True)
except KeyError:
pass
try:
df['IIRS_subscale4'] = df[['IIRS4', 'IIRS5', 'IIRS10']].mean(axis=1).where(~df[['IIRS4', 'IIRS5', 'IIRS10']].isna().any(axis=1))
except KeyError:
pass
return df
def read_stata_file(file_path: str) -> pd.DataFrame:
"""
Read data from Stata file.
Parameters:
- file_path (str): Path of the Stata file.
Returns:
- pd.DataFrame: DataFrame containing the data.
"""
df = pd.read_stata(file_path)
return df
if __name__ == "__main__":
file_path = "your_file.dta"
df = read_stata_file(file_path)
df = calculate_IIRS_scores(df)
```
Note that I have also added a new function `read_stata_file` to read the Stata file, as it is good practice to separate different concern into different functions.
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