df.info()>><class'pandas.core.frame.DataFrame'>RangeIndex:6entries,0to5Datacolumns(total4columns):# Column Non-Null Count Dtype---0a6non-nullint641b6non-nullbool2c6non-nullfloat643d6non-nullobjectdtypes:bool(1),float64(1),int64(1),object(1)memory usage:278.0+bytes 2、转换数值类型 数...
boolean_col bool custom object dtype: object 当然了我们也可以调用info()方法来实现上述的目的,代码如下 df.info() output <class 'pandas.core.frame.DataFrame'> RangeIndex: 4 entries, 0 to 3 Data columns (total 8 columns): # Column Non-Null Count Dtype --- --- --- --- 0 string_col ...
虽然Series类似于 ndarray,如果你需要一个实际的ndarray,那么请使用Series.to_numpy()。 代码语言:javascript 代码运行次数:0 运行 复制 In [20]: s.to_numpy() Out[20]: array([ 0.4691, -0.2829, -1.5091, -1.1356, 1.2121]) 即使Series由ExtensionArray支持,Series.to_numpy()将返回一个 NumPy ndarray。
#ColumnNon-NullCountDtype --- 0a6non-nullint64 1b6non-nullbool 2c6non-nullfloat64 3d6non-nullobject dtypes:bool(1),float64(1),int64(1),object(1) memoryusage:278.0+bytes 1. 2. 3. 4. 5. 6. 7. 8. 9. 10. 11. 12. 2、转换数值类型 数值类型包括int和...
一:pandas简介 Pandas 是一个开源的第三方 Python 库,从 Numpy 和 Matplotlib 的基础上构建而来,享有数据分析“三剑客之一”的盛名(NumPy、Matplotlib、Pandas)。Pandas 已经成为 Python 数据分析的必备高级工具,它的目标是成为强大、
df.info()"""<class 'pandas.core.frame.DataFrame'>RangeIndex: 1000000 entries, 0 to 999999Data columns (total 14 columns): # Column Non-Null Count Dtype --- --- --- --- 0 CID 1000000 non-null object 1 Name 1000000 non-null object 2 Age 1000...
->1121returnself._get_value(key)1123# Convert generator to list before going through hashable part1124# (We will iterate through the generator there to check for slices)1125ifis_iterator(key): File ~/work/pandas/pandas/pandas/core/series.py:1237,inSeries._get_value(self, label, takeable)...
In [7]: df.info(memory_usage="deep") <class 'pandas.core.frame.DataFrame'> RangeIndex: 5000 entries, 0 to 4999 Data columns (total 8 columns): # Column Non-Null Count Dtype --- --- --- --- 0 int64 5000 non-null int64 1 float64 5000 non-null float64 2 datetime64[ns] 5000...
- Convert to float type """ new_val = val.replace(',','').replace('$', '') return float(new_val) 1. 2. 3. 4. 5. 6. 7. 8. 9. 该代码使用 python 的字符串函数去除“$”和“,”,然后将值转换为浮点数 也许有人会建议使用 Decimal 类型的货币。但这不是 pandas 中的内置数据类型...
convert_categoricals bool,默认为 True。将分类列转换为 pd.Categorical。 df = pd.read_stata('filename.dta') 写 DataFrame.to_stata( path , ...) 将DataFrame 写入 Stata 数据集文件 df = pd.DataFrame({'animal': ['falcon', 'parrot', 'falcon','parrot'], 'speed': [350, 18, 361, ...