Python pandas: check if any value is NaN in DataFrame # 查看每一列是否有NaN:df.isnull().any(axis=0)# 查看每一行是否有NaN:df.isnull().any(axis=1)# 查看所有数据中是否有NaN最快的:df.isnull().values.any()# In [2]: df = pd.DataFrame(np.random.randn(1000,1000))In [3]: df[d...
以便更好地理解。pd.isna(cell_value)可用于检查给定单元格值是否为nan。或者，pd.notna(cell_value)...
com/questions/29530232/python-pandas-check-if-any-value-is-nan-in-dataframe sh.isnull().values.any() # 数据很完整 代码语言:javascript 代码运行次数:0 运行 AI代码解释 False 代码语言:javascript 代码运行次数:0 运行 AI代码解释 sh.isnull().values.sum() # 没有值 代码语言:javascript 代码运行...
dtype: float64 In [73]: df["three"] = df["one"] * df["two"] In [74]: df["flag"] = df["one"] > 2 In [75]: df Out[75]: one two three flag a 1.0 1.0 1.0 False b 2.0 2.0 4.0 False c 3.0 3.0 9.0 True d NaN 4.0 NaN False ...
# Random integersarray = np.random.randint(20, size=12)arrayarray([ 0, 1, 8, 19, 16, 18, 10, 11, 2, 13, 14, 3])# Divide by 2 and check if remainder is 1cond = np.mod(array, 2)==1condarray([False, True, False, True, False, ...
在dataframe中为np.nan或者pd.naT(缺失时间),在series中为none或者nan即可。pandas使用浮点NaN (Not a Number)表示浮点和非浮点数组中的缺失数据,它只是一个便于被检测出来的标记而已。pandas primarily uses the value np.nan to represent missing data. It is bydefault not included incomputations. ...
我利用pivot和set_index,把不需要处理的columns先暂时设置成index,这样仅仅留下来两列作为新生成的列的column name和value,完成后在reset_index即可。 # 下面是把行转成列 # 提取保持不变的列,未来要暂时作为index index_col = [item for item in df_Tableau.keys() if item not in ['Measurement', 'Data...
从pandas数据框中选择指定列不全为NaN的行我想选择所有符合以下条件的行：'b'、'c'、'g'这几列的...
[key] 1120 elif key_is_scalar: -> 1121 return self._get_value(key) 1123 # Convert generator to list before going through hashable part 1124 # (We will iterate through the generator there to check for slices) 1125 if is_iterator(key): File ~/work/pandas/pandas/pandas/core/series.py:...
if outliers.any(): print(f"Outliers detected: {outliers}") 数据质量报告:def generate_data_quality_report(df): report = { 'total_rows': len(df), 'missing_values': df.isnull().sum().sum(), 'duplicate_rows': df.duplicated().sum(), 'data_types': df.dtypes.value_counts().to_dict...