如何将pandas数据帧的单个列转换为字符串类型?在下面的住房数据的df我需要将zipcode转换为字符串,以便当我运行线性回归时,zipcode被视为分类而不是数字。谢谢!
df = pd.DataFrame({'zipcode': {17384: 98125, 2680: 98107, 722: 98005, 18754: 98109, 14554: 98155}, 'bathrooms': {17384: 1.5, 2680: 0.75, 722: 3.25, 18754: 1.0, 14554: 2.5}, 'sqft_lot': {17384: 1650, 2680: 3700, 722: 51836, 18754: 2640, 14554: 9603}, 'bedrooms': {17384: 2, 2680: 2, 722: 4, 18754: 2, 14554: 4}, 'sqft_living': {17384: 1430, 2680: 1440, 722: 4670, 18754: 1130, 14554: 3180}, 'floors': {17384: 3.0, 2680: 1.0, 722: 2.0, 18754: 1.0, 14554: 2.0}})
print (df)
bathrooms bedrooms floors sqft_living sqft_lot zipcode
722 3.25 4 2.0 4670 51836 98005
2680 0.75 2 1.0 1440 3700 98107
14554 2.50 4 2.0 3180 9603 98155
17384 1.50 2 3.0 1430 1650 98125
18754 1.00 2 1.0 1130 2640 98109
答案 0 :(得分:30)
您需要astype
:
df['zipcode'] = df.zipcode.astype(str)
#df.zipcode = df.zipcode.astype(str)
转换为categorical
:
df['zipcode'] = df.zipcode.astype('category')
#df.zipcode = df.zipcode.astype('category')
另一个解决方案是Categorical
:
df['zipcode'] = pd.Categorical(df.zipcode)
数据样本:
import pandas as pd
df = pd.DataFrame({'zipcode': {17384: 98125, 2680: 98107, 722: 98005, 18754: 98109, 14554: 98155}, 'bathrooms': {17384: 1.5, 2680: 0.75, 722: 3.25, 18754: 1.0, 14554: 2.5}, 'sqft_lot': {17384: 1650, 2680: 3700, 722: 51836, 18754: 2640, 14554: 9603}, 'bedrooms': {17384: 2, 2680: 2, 722: 4, 18754: 2, 14554: 4}, 'sqft_living': {17384: 1430, 2680: 1440, 722: 4670, 18754: 1130, 14554: 3180}, 'floors': {17384: 3.0, 2680: 1.0, 722: 2.0, 18754: 1.0, 14554: 2.0}})
print (df)
bathrooms bedrooms floors sqft_living sqft_lot zipcode
722 3.25 4 2.0 4670 51836 98005
2680 0.75 2 1.0 1440 3700 98107
14554 2.50 4 2.0 3180 9603 98155
17384 1.50 2 3.0 1430 1650 98125
18754 1.00 2 1.0 1130 2640 98109
print (df.dtypes)
bathrooms float64
bedrooms int64
floors float64
sqft_living int64
sqft_lot int64
zipcode int64
dtype: object
df['zipcode'] = df.zipcode.astype('category')
print (df)
bathrooms bedrooms floors sqft_living sqft_lot zipcode
722 3.25 4 2.0 4670 51836 98005
2680 0.75 2 1.0 1440 3700 98107
14554 2.50 4 2.0 3180 9603 98155
17384 1.50 2 3.0 1430 1650 98125
18754 1.00 2 1.0 1130 2640 98109
print (df.dtypes)
bathrooms float64
bedrooms int64
floors float64
sqft_living int64
sqft_lot int64
zipcode category
dtype: object
答案 1 :(得分:4)
对于熊猫> = 1.0,现在有一个专用的字符串数据类型:
1)您可以使用.astype('string')将列转换为此熊猫 string数据类型:
df['zipcode'] = df['zipcode'].astype('string')
2),这与使用str
设置熊猫对象数据类型的不同:
df['zipcode'] = df['zipcode'].astype(str)
3)要更改为类别数据类型,请使用:
df['zipcode'] = df['zipcode'].astype('category')
当您查看数据框的信息时,可以看到数据类型的这种差异:
df = pd.DataFrame({
'zipcode_str': [90210, 90211] ,
'zipcode_string': [90210, 90211],
'zipcode_category': [90210, 90211],
})
df['zipcode_str'] = df['zipcode_str'].astype(str)
df['zipcode_string'] = df['zipcode_str'].astype('string')
df['zipcode_category'] = df['zipcode_category'].astype('category')
df.info()
# you can see that the first column has dtype object
# while the second column has the new dtype string
# the third column has dtype category
# Column Non-Null Count Dtype
--- ------ -------------- -----
0 zipcode_str 2 non-null object
1 zipcode_string 2 non-null string
2 zipcode_category 2 non-null category
dtypes: category(1), object(1), string(1)
从文档中:
'string'扩展类型解决了object-dtype的几个问题 NumPy数组:
1)您可能会意外地将字符串和非字符串的混合存储在 对象dtype数组。一个StringArray只能存储字符串。
2)对象dtype中断dtype特定的操作,例如 DataFrame.select_dtypes()。没有一种清晰的方法可以只选择文字 同时排除非文本列,但仍然是object-dtype列。
3)读取代码时,对象dtype数组的内容不太清楚 比字符串。
有关熊猫1.0的信息,请参见:
https://pandas.pydata.org/pandas-docs/version/1.0.0/whatsnew/v1.0.0.html
答案 2 :(得分:4)
针对标称数据(例如无序)的先前答案。如果有理由对序数变量强加顺序,则可以使用:
# Transform to category
df['zipcode_category'] = df['zipcode_category'].astype('category')
# Add ordered category
df['zipcode_ordered'] = df['zipcode_category']
# Setup the ordering
df.zipcode_ordered.cat.set_categories(
new_categories = [90211, 90210], ordered = True, inplace = True
)
# Output IDs
df['zipcode_ordered_id'] = df.zipcode_ordered.cat.codes
print(df)
# zipcode_category zipcode_ordered zipcode_ordered_id
# 90210 90210 1
# 90211 90211 0
有关设置排序类别的更多详细信息,可以在pandas网站上找到:
https://pandas.pydata.org/pandas-docs/stable/user_guide/categorical.html#sorting-and-order
答案 3 :(得分:1)
要将列转换为字符串类型(在pandas中将是 object 列本身),请使用#top-elements .large.custom-button span,
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:
astype
如果您想获得df.zipcode = zipcode.astype(str)
列,可以将参数Categorical
传递给函数:
'category'