print_line() # str.contains 字符串包含查询; 经常用在长字符串中; print(df.loc[df['key'].str.contains('A'), :]) """ key data 0 A 0 3 A 5 6 A 10 """ print_line() # where, 不满足条件的被赋值(默认赋空值) cond = df['key'] =='A' print(df['key'].where(cond, inplac...
>>>codes,uniques=pd.factorize(np.array(['b','b','a','c','b'],dtype="O"))>>>codesarray([0,0,1,2,0])>>>uniquesarray(['b','a','c'],dtype=object) or and in string regex use | as or df.columns[df.columns.str.contains('rnk|rank') where np.where, condition, if true...
input总是返回一个 * string *，但是因为panda读取的ID列有一个数字dtype，当你用字符串过滤它时，你会得到一个空的 Dataframe 。您需要使用int将value/ID（由用户输入）转换为 * number *。试试这个：
我们将首先导入 pandas 模块,然后从 zillow.com 中将房价数据集读取到 Jupyter 笔记本中。 首先,我们探索 Pandas 的filter方法来过滤数据。 我们可以使用filter方法过滤列。 为此,我们需要将列作为列表传递给filter方法的items参数,如下所示: 代码语言:javascript 复制 filtered_data = data.filter(items=['State', '...
8.apply函数用非常复杂的条件，很多的if else 比如 defabcd_to_e(x):ifx['a']>1:return1elifx[...
09 数据处理:Filter、Sort 代码语言:javascript 复制 # 保留小数位,四舍六入五成双 df.round(2) # 全部 df.round({'A': 1, 'C': 2}) # 指定列 df['Name'] = df.Name # 取列名的两个方法 df[df.index == 'Jude'] # 按索引查询要用 .index df[df[col] > 0.5] # 选择col列的值大于0.5...
Suppose, we have a DataFrame that contains a string-type column and we need to filter the column based on a substring, if the value contains that particular substring, we need to replace the whole string. Pandas - Replacing whole string if it contains substring ...
Filter by logical operators:df.values, df.name, etc. Filter by list of values:isin() Filter by string:str.startswith(), str.endswith() or str.contains() Filter based on query:query() Filter by largest or smallest value for specified column:nlargest() or nsmallest() ...
equals() Returns True if two DataFrames are equal, otherwise False eval Evaluate a specified string explode() Converts each element into a row ffill() Replaces NULL values with the value from the previous row fillna() Replaces NULL values with the specified value filter() Filter the DataFram...
na_filter=True, verbose=False, skip_blank_lines=True, parse_dates=False, infer_datetime_format=False, keep_date_col=False, date_parser=None, dayfirst=False, cache_dates=True, iterator=False, chunksize=None, compression='infer', thousands=None, decimal: 'str' = '.', lineterminator=None, ...