If you have a DataFrame with all string columns holding integer values, you can simply convert it to int dtype using as below. If you have any column that has alpha-numeric values, this returns an error. If you run this on our DataFrame, you will get an error. # Convert all columns ...
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"...
After using to_numeric() function x int64 y int64 dtype: object In the above code, the apply() function is used along with the to_numeric() function on the given DataFrame to convert multiple columns into int at once. Further reading: Pandas convert column to float Read more → Get ...
np.nan)) In [8]: sdf.head() Out[8]: 0 1 2 3 0 NaN NaN NaN NaN 1 NaN NaN NaN NaN 2 NaN NaN NaN NaN 3 NaN NaN NaN NaN 4 NaN NaN NaN NaN In [9]: sdf.dtypes Out[9]: 0 Sparse[float64, nan] 1 Sparse
未来,我们建议避免使用 .values,而是使用 .array 或.to_numpy()。.values 有以下缺点: 当你的 Series 包含一个扩展类型时,不清楚 Series.values 返回一个 NumPy 数组还是扩展数组。Series.array 总是返回一个 ExtensionArray,并且永远不会复制数据。Series.to_numpy() 总是返回一个 NumPy 数组,可能会造成复制/...
It converts all the PandasDataFramecolumns toint. Convert Pandas DataFrame Column tointWith Rounding Off We can round off thefloatvalue tointby usingdf.round(0).astype(int). importpandasaspdimportnumpyasnp df=pd.DataFrame(np.random.rand(5,5)*5)print("*** Random Float DataFrame ***...
to_excel('学生成绩汇总表.xlsx', index=False) # 重新读取Excel文件并打印结果 df = pd.read_excel('学生成绩汇总表.xlsx') print(df) 实例10:数据分箱:对数据进行分箱统计 import pandas as pd # 首先创建一个空的DataFrame df = pd.DataFrame(columns=['分箱']) # 然后建立一个列表数据,列表里面是...
we want toconvert the entire DataFrame, for this purpose, we have a method calledpandas.to_numeric()method but again it fails in case of float values and hence we have to loop over the columns of the DataFrame to change the data type to float first then we will convert them to int. ...
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...
0 to 3# Data columns (total 4 columns):# # Column Non-Null Count Dtype# --- --- --- ---# 0 id 4 non-null int64# 1 name 4 non-null object# 2 experience 4 non-null int64# 3 salary 4 non-null float64# dtypes: float64(1), int64(2), object(1)# memory usage: 256.0+ ...