# 检查缺失值df.isnull() # 删除有缺失值的行df.dropna()# 用特定值填充缺失值df.fillna(value) # 插入缺失值df.interpolate()# 检查重复行df.duplicated()# 删除重复行df.drop_duplicates()# 计算z分数z_scores = (df - df.mean()) / df.std()# 根据z分数识别离群值 =
def soc_iter(TEAM,home,away,ftr): #team, row['HomeTeam'], row['AwayTeam'], row['FTR'] if [((home == TEAM) & (ftr == 'D')) | ((away == TEAM) & (ftr == 'D'))]: result = 'Draw' elif [((home == TEAM) & (ftr != 'D')) | ((away == TEAM) & (ftr != ...
isin(ids), 'assigned_name'] = "some new value" 过滤条件是外部函数 代码语言:python 代码运行次数:0 运行 AI代码解释 """example of applying a complex external function to each row of a data frame""" def stripper(x): l = re.findall(r'[0-9]+(?:\.[0-9]+){3}', x['Text with...
蓝因子 123个Pandas常用基础指令，真香！1. 导入模块 import pandas as pd import numpy as np 2. 读取数据和保存数据 2.1 从CSV文件读取数据,编码'gbk'2.2 读取前6行,当数据量比较大时，可以只读取前n行 2.3 第一列作为行索引,忽略列索引 2.4 读取时忽略第1/3/5行和最后两行 2.5 从限定分隔符...
(self, key, value) 1284 ) 1285 1286 check_dict_or_set_indexers(key) 1287 key = com.apply_if_callable(key, self) -> 1288 cacher_needs_updating = self._check_is_chained_assignment_possible() 1289 1290 if key is Ellipsis: 1291 key = slice(None) ~/work/pandas/pandas/pandas/core/...
1 2 df.loc[df['column_name'] != 'some_value'] df.loc[~df['column_name'].isin('some_values')] #~取反 if values are str, remember to pass a list ['str1','str2']在字符串pandas列中查找多个关键字的更有效方法示例(也就是上面第2个方法)1 2 3 4 5 6 7 8 9 10 11 12 13 ...
process(row) # 终极武器 → 转换为NumPy数组 numpy_array = df.values ``` 💥 踩坑血泪史(新手必看的避雷指南) ⚠️ SettingWithCopyWarning地狱 当你看到这个警告时!(90%的Pandas新手都会栽跟头) 错误示范: python subset = sales_data[sales_data['促销']] ...
if col_mapping:df.rename(columns=col_mapping, inplace=True)df.to_excel(writer, sheet_name=sheet...
df_drop_col = df.drop('B', axis=1)df_drop_rows = df.drop(['row1','row3'])df_mixed = df.drop(columns=['A'], index=['row2'])删除操作后的数据变更需要特别关注索引变化。当删除行时，剩余行的索引会自动重组；删除列则会导致列标签集合发生改变。在处理调查问卷数据时，常需要删除无效问卷...
data.loc[data.shape[0]] = row 1. 2. 3. 4. 5. 6. 7. 8. 9. 10. 如果想在起始位置添加一行,可以使用如下方式 c1 = ['a', 'a', 'c', 'd'] c2 = [1, 2, 3, 4] c3 = ['0.1', '0.3', '0.5', '0.7'] data = pd.DataFrame({'c1': c1, 'c2': c2, 'c3': c3}) ...