Name: column name, dtype: float64 df['column name'] = df['column name'].astype(np.int64) ValueError:无法将非有限值(NA或INF)转换为整数 #http://pandas.pydata.org/pandas-docs/stable/user_guide/integer_na.html df['column nam
Python program to round when converting float to integer# Importing pandas package import pandas as pd # Importing numpy package import numpy as np # Creating a dictionary d = {'a':[4.5,6.7,6.4,2.4,7.5]} # Creating a DataFrame df = pd.DataFrame(d) # Display Original df print("Original...
Use pandas DataFrame.astype(int) and DataFrame.apply() methods to cast float column to integer(int/int64) type. I believe you would know float is bigger
importpandasaspd# 创建一个包含异常值的 DataFramedata={'column1':['1','two','3','4']}df=pd.DataFrame(data)# 使用 pandas 的 to_numeric 方法处理异常,将无法转换的设置为 NaNdf['column1']=pd.to_numeric(df['column1'],errors='coerce').astype(float)print(df) Python Copy Output: 示例5:...
使用pandas的astype方法将指定列的数据类型转换为int。 python # 将指定列的数据类型转换为int df[column_to_convert] = df[column_to_convert].astype(int) 更新CSV文件,将转换后的int类型数据写回: 使用pandas的to_csv方法将更新后的DataFrame写回到CSV文件中。 python # 将更新后的DataFrame写回到CSV文件 ...
By using pandas DataFrame.astype() and pandas.to_numeric() methods you can convert a column from string/int type to float. In this article, I will explain
pandas 0.24+ 转换带有缺失值的数字的解决方案: df = pd.DataFrame({'column name':[7500000.0,7500000.0, np.nan]}) print (df['column name']) 0 7500000.0 1 7500000.0 2 NaN Name: column name, dtype: float64 df['column name'] = df['column name'].astype(np.int64) ValueError:无法将非有限...
This tutorial will focus on converting an object-type column to float in Pandas. Convert an Object-Type Column to Float in Pandas An object-type column contains a string or a mix of other types, whereas a float contains decimal values. We will work on the following DataFrame in this articl...
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 ***")print(df)print("***")print("***")print("*** ...
(pd.to_numeric,errors='ignore'))# <class 'pandas.core.frame.DataFrame'># RangeIndex: 4 entries, 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...