如果要创建一个DataFrame,可以直接通过dtype参数指定类型: df = pd.DataFrame(a, dtype='float')#示例1df = pd.DataFrame(data=d, dtype=np.int8)#示例2df = pd.read_csv("somefile.csv", dtype = {'column_name': str}) 对于单列或者Series 下面是一个字符串Seriess的例子,它的dtype为object: >>>...
will also try to change non-numeric objects (such as strings) into integers or floating-point numbers as appropriate.to_numeric()input can be aSeriesor a column of adataFrame. If some values can’t be converted to a numeric type,to_numeric()allows us to force non-numeric values to ...
data.iloc[:,1] # second column of data frame (last_name) 数据帧的第二列(last_name) data.iloc[:,-1] # last column of data frame (id) 数据帧的最后一列(id) 可以使用.iloc索引器一起选择多个列和行。 1 2 3 4 5 # Multiple row and column selections using iloc and DataFrame 使用iloc...
dtype: datetime64[ns] In [566]: store.select_column("df_dc", "string") Out[566]: 0 foo 1 foo 2 foo 3 foo 4 NaN 5 NaN 6 foo 7 bar Name: string, dtype: object
Series s.loc[indexer] DataFrame df.loc[row_indexer,column_indexer] 基础知识 如在上一节介绍数据结构时提到的,使用[](即__getitem__,对于熟悉在 Python 中实现类行为的人)进行索引的主要功能是选择较低维度的切片。以下表格显示了使用[]索引pandas 对象时的返回类型值: 对象类型 选择 返回值类型 Series seri...
url='https://raw.githubusercontent.com/datasets/investor-flow-of-funds-us/master/data/weekly.csv'df=pd.read_csv(url)df.head() Step 4. What is the frequency of the dataset? weekly Step 5. Set the column Date as the index. df = df.set_index('Date') ...
DataFrame'> RangeIndex: 193 entries, 0 to 192 Data columns (total 6 columns): # Column Non-Null Count Dtype --- --- --- --- 0 country 193 non-null object 1 beer_servings 193 non-null int64 2 spirit_servings 193 non-null int64 3 wine_servings 193 non-null int64 4 total_litres...
-> 4133 new_data = self._mgr.take(4134 indices,4135 axis=self._get_block_manager_axis(axis),4136 verify=True,4137 )4138 return self._constructor_from_mgr(new_data, axes=new_data.axes).__finalize__(4139 self, method="take"4140 )File ~/work/pandas/pandas/pandas/core/internals/managers...
df[column].unique() 1. 查看后 x 行的数据 # Getting last x rows. df.tail(5) 1. 2. 跟head 一样,我们只需要调用 tail 并且传入想要查看的行数即可。注意,它并不是从最后一行倒着显示的,而是按照数据原来的顺序显示。 修改列名 输入新列名即可 ...
1.866184 -1.446617 0.036660two -1.660522 0.929553 -1.298649three 3.565769 0.682402 1.041927# the levels are automatically included as data columnsIn [512]: store.select("df_mi", "foo=bar")Out[512]:A B Cfoo barbar one 0.410395 0.618321 0.560398two 1.434027 -0.033270 0.343197 ...