You can use pandasDataFrame.astype()function to convert column to int(integer). You can apply this to a specific column or to an entire DataFrame. To cast the data type to a 64-bit signed integer, you can use numpy.int64, numpy.int_, int64, or int as param. To cast to a32-bit ...
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: 代码语言:javascript 代码运行次数:0 运行 AI代码解释 >>>s=pd.Series(['1'...
If some NaN s in columns need replace them to some int (eg 0 ) by fillna , because type NaN 是float: df = pd.DataFrame({'column name':[7500000.0,np.nan]}) df['column name'] = df['column name'].fillna(0).astype(np.int64) print (df['column name']) 0 7500000 1 0 Name: ...
dtype: int64 Explanation The Pandas library is imported. A Series is created using the pd.Series() function. The to_numeric() function is used to convert the string values of the Series into appropriate integer values. If you use floating numbers rather than int then column will be converte...
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...
步骤4 每一列(column)的数据类型是什么样的？# 运行以下代码crime.info()<class 'pandas.core.frame.DataFrame'>RangeIndex: 55 entries, 0 to 54Data columns (total 12 columns): # Column Non-Null Count Dtype--- --- --- --- 0 Year 55 non-null int64 1 Population ...
# 运行以下代码deffix_century(x): year = x.year - 100if x.year > 1989else x.yearreturn datetime.date(year, x.month, x.day)# apply the function fix_century on the column and replace the values to the right onesdata['Yr_Mo_Dy'] = data['Yr_Mo_Dy'].apply(fix_century)# data...
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:
In [7]: d = {"b": 1, "a": 0, "c": 2} In [8]: pd.Series(d) Out[8]: b 1 a 0 c 2 dtype: int64 如果传递了索引,则将从数据中与索引中的标签对应的值提取出来。 代码语言:javascript 代码运行次数:0 运行 复制 In [9]: d = {"a": 0.0, "b": 1.0, "c": 2.0} In [...
df.info()输出：<class 'pandas.core.frame.DataFrame'>RangeIndex:3 entries, to 2Data columns (total 2 columns):# Column Non-Null Count Dtype --- --- --- --- Name 3 non-null object1 Age 3 non-null int64 dtypes: int64(1), object(1)memory usage: 180...