# Convert multiple columnsdf=df.astype({'Fee':'float','Discount':'float'})print("Convert multiple columns to float type:")print("Type of the columns:\n",df.dtypes)# Output:# Convert multiple columns to float type:# Type of the columns:# Fee float64# Discount float64# dtype: object ...
In [1]: dates = pd.date_range('1/1/2000', periods=8) In [2]: df = pd.DataFrame(np.random.randn(8, 4), ...: index=dates, columns=['A', 'B', 'C', 'D']) ...: In [3]: df Out[3]: A B C D 2000-01-01 0.469112 -0.282863 -1.509059 -1.135632 2000-01-02 1.212112...
Example 2: Convert Multiple pandas DataFrame Columns from Integer to Float It is also possible to transform multiple variables to a different data type. In Example 2, I’ll show how to change the data class of two variables from integer to float. ...
'2017','2018','2019'],'Inflation Rate':['4.47','5','5.98','4.1']}# create a dataframedf = pd.DataFrame(Data)# converting each value# of column to a stringdf['Inflation Rate'] = df['Inflation Rate'].astype(float)# show the dataframeprint(df)# show the datatypesprint(df.dtypes)...
在pandas read_csv中将百分比字符串转换为浮点数Pandas 可以在字符串列上使用 Python 的字符串处理功能。
Theto_numeric()method converts the supplied argument to a numeric type. The default returndtypeisfloat64orint64depending on the supplied data. Notice that the values in the integer columns got converted toint64and the values in the float columns got converted tofloat64. ...
Similarly, you can also convert multiple columns from float to integer by sendingdict of column name -> data typetoastype()method. The below example converts both columnsFeeandDiscount to int types. # Converting "Fee" and "Discount" from float to intdf=df.astype({"Fee":"int","Discount"...
What if one of the columns is not a string? Then you will get error like: TypeError: can only concatenate str (not "float") to str To avoid this error you can convert the column by using method.astype(str): df['Magnitude Type'] +', '+ df['Magnitude'].astype(str) ...
downcast: It is a string parameter. It has four options:integer,signed,unsigned, orfloat. An example of converting the object type to float usingto_numeric()is shown below. importpandasaspd df=pd.DataFrame([["10.0",6,7,8],["1.0",9,12,14],["5.0",8,10,6]],columns=["a","b",...
df['price'] = df['price'].astype('Int64') # convert data to int. return df 我收到一个错误:对象无法转换为IntegerDtype。 我试着解决这个问题,就像前面在一个SoF问题中提到的那样,首先转换为float,然后转换为int: def convert_price(df):