By using pandasDataFrame.astype()andpandas.to_numeric()methods you can convert a column from string/int type to float. In this article, I will explain how to convert one or multiple string columns to float type using examples. Advertisements Key Points – Usepd.to_numeric()to convert a col...
importpandasaspd# 创建一个包含浮动数据的Seriesdata = pd.Series([1.5,2.5,3.5,4.5])# 使用 pd.to_numeric() 方法将数据转换为整数,并且下行缩减内存numeric_data = pd.to_numeric(data, downcast='integer')# 输出转换后的结果print(numeric_data) 4)用于 DataFrame importpandasaspd# 创建DataFramedf = pd...
# Convert data type of Order Date column to datedf["Order Date"] = pd.to_datetime(df["Order Date"])to_numeric()可以将列转换为数字数据类型(例如，整数或浮点数)。# Convert data type of Order Quantity column to numeric data typedf["Order Quantity"] = pd.to_numeric(df["Order Quantity"]...
0to1Datacolumns(total3columns):# Column Non-Null Count Dtype---0year2non-nullint641month2non-nullobject2day2non-nullint64dtypes:int64(2),object(1)memory usage:176.0+bytes 此外这里再延伸一下,去掉
例如TypeError: Could not convert ace to numeric），那么你可能有pandas>=2.0。
3.2. pd.to_numeric转化为数字类型 3.3. pd.to_timedelta转化为时间差类型 4. 智能判断数据类型 5. 数据类型筛选 1. 加载数据时指定数据类型 一般来说,为了省事我都是直接pd.DataFrame(data)或pd.read_xx(filename)就完事了。 比如:(下面数据大家直接拷贝后读取剪切板即...
# Convert inefficient dtypes df['date_column'] = pd.to_datetime(df['date_column']) df['numeric_column'] = pd.to_numeric(df['numeric_column'], downcast='float') print(df.dtypes) 输出 date_column datetime64[ns] numeric_column float32 ...
df['mix_col'] = pd.to_numeric(df['mix_col'], errors='coerce') df output 而要是遇到缺失值的时候,进行数据类型转换的过程中也一样会出现报错,代码如下 df['missing_col'].astype('int') output ValueError: Cannot convert non-finite values (NA or inf) to integer ...
方法二:使用to_numeric()将对象转为浮点数 以下代码显示了如何使用to_numeric()函数将 DataFrame 中的点列从对象转换为浮点数: #convert points columnfromobjecttofloatdf['points'] = pd.to_numeric(df['points'], errors='coerce') #view updated DataFrame ...
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 converted to float. 1 2 3 4 5 6 import pandas as pd x=pd.Series(['3.5',5.2,'8',4.2,'9']) print(x) ...