本文介紹了 pandas 數(shù)據(jù)框?qū)⒘蓄愋娃D(zhuǎn)換為字符串或分類的處理方法，對(duì)大家解決問(wèn)題具有一定的參考價(jià)值，需要的朋友們下面隨著小編來(lái)一起學(xué)習(xí)吧！
問(wèn)題描述
如何將 pandas 數(shù)據(jù)框的單列轉(zhuǎn)換為字符串類型?在下面的住房數(shù)據(jù) df 中，我需要將郵政編碼轉(zhuǎn)換為字符串，以便在運(yùn)行線性回歸時(shí)，郵政編碼被視為分類而不是數(shù)字.謝謝！
How do I convert a single column of a pandas dataframe to type string? In the df of housing data below I need to convert zipcode to string so that when I run linear regression, zipcode is treated as categorical and not numeric. Thanks!
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
推薦答案
你需要astype:
df['zipcode'] = df.zipcode.astype(str)
#df.zipcode = df.zipcode.astype(str)
<小時(shí)>
用于轉(zhuǎn)換為分類:
df['zipcode'] = df.zipcode.astype('category')
#df.zipcode = df.zipcode.astype('category')
另一種解決方案是分類:
Another solution is Categorical:
df['zipcode'] = pd.Categorical(df.zipcode)
數(shù)據(jù)樣本:
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
這篇關(guān)于 pandas 數(shù)據(jù)框?qū)⒘蓄愋娃D(zhuǎn)換為字符串或分類的文章就介紹到這了，希望我們推薦的答案對(duì)大家有所幫助，也希望大家多多支持html5模板網(wǎng)！
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