导入pandas库:import pandas as pd 创建数据帧:df = pd.DataFrame({'column_name': ['string1', 'string2', 'string3']}) 使用正则表达式进行匹配、替换和提取: 匹配:df['column_name'].str.contains(r'pattern1|pattern2') 替换:df['column_name'].
df['Column'].value_counts() 使用方式: 使用value_counts计算某列中每个唯一值的频率。 示例: 计算“Status”列中每个状态的数量。 代码语言:javascript 代码运行次数:0 运行 AI代码解释 df['Status'].value_counts() 40. 使用str.contains进行模糊匹配 代码语言:javascript 代码运行次数:0 运行 AI代码解释 df...
方法一:创建时,显式请求stringdtype即:pd.Series(data,dtype="string")或者dtype=pd.StringDtype(),这种方式和np.array()里面显示指定数据类型完全一样。 方法二:Series=Series.astype("string") or astype(pd.StringDtype()) Note:astype用处广泛:astype(int|float|"int"|"float32"等) 2、字符串处理: 在...
fromsqlalchemyimportcreate_engineimportpolarsaspl query ="SELECT * FROM user_to_role"engine = create_engine("mysql+pymysql://")# 这里我们将 user_id 改成了字符串,当然我们改成字符串反而是不对的,这里只是演示这个功能df = pl.read_database(query, engine, schema_overrides={"user_id": pl.Str...
set_index('column_one') # 更改索引列 df.rename(index=lambda x: x + 1) # 批量重命名索引 # 重新命名表头名称 df.columns = ['UID', '当前待打款金额', '认证姓名'] df['是否设置提现账号'] = df['状态'] # 复制一列 df.loc[:, ::-1] # 列顺序反转 df.loc[::-1] # 行顺序反转...
pd.read_sql(query, connection_object) #从 JSON 格式的字符串导入数据 pd.read_json(json_string) # 解析 URL、字符串或者 HTML 文件,抽取其中的 tables 表格 pd.read_html(url) # 从你的粘贴板获取内容,并传给 read_table() pd.read_clipboard() ...
`pandas.arrays.StringArray` or:class:`pandas.arrays.ArrowStringArray`:class:`bool` :class:`pandas.arrays.BooleanArray`===The ExtensionArray created when the scalar type is :class:`str` is determined by``pd.options.mode.string_storage`` if the dtype is not explicitly given.For all other ca...
df=df[df['name'].str.contains('ar',case=False)] Output id name class mark 2 3 Arnold1 #Three 55 Delete the rows having matching sub-string in one column. my_str='abcd' df=df[~df['col1'].str.contains(my_str)] #df=df[~df.index.str.contains('\?')] # index column havin...
In the query version though, we just type the name of the column. Moreover, when we use the Pandas query method, we can use the method in a "chain" of Pandas methods, similar to how you usepipes in R's dplyr. Ultimately, using the query method is easier to write, easier to read...
droplevel() Drops the specified index/column(s) dropna() Drops all rows that contains NULL values dtypes Returns the dtypes of the columns of the DataFrame duplicated() Returns True for duplicated rows, otherwise False empty Returns True if the DataFrame is empty, otherwise False eq() Returns ...