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DataFrame對(duì)象的創(chuàng)建,修改,合并
import pandas as pd import numpy as np
創(chuàng)建DataFrame對(duì)象
# 創(chuàng)建DataFrame對(duì)象 df = pd.DataFrame([1, 2, 3, 4, 5], columns=['cols'], index=['a','b','c','d','e']) print df
cols a 1 b 2 c 3 d 4 e 5
df2 = pd.DataFrame([[1, 2, 3],[4, 5, 6]], columns=['col1','col2','col3'], index=['a','b']) print df2
col1 col2 col3 a 1 2 3 b 4 5 6
df3 = pd.DataFrame(np.array([[1,2],[3,4]]), columns=['col1','col2'], index=['a','b']) print df3
col1 col2 a 1 2 b 3 4
df4 = pd.DataFrame({'col1':[1,3],'col2':[2,4]},index=['a','b']) print df4
col1 col2 a 1 2 b 3 4
創(chuàng)建DataFrame對(duì)象的數(shù)據(jù)可以為列表,數(shù)組和字典,列名和索引為列表對(duì)象
基本操作
# DataFrame對(duì)象的基本操作 df2.index
Index([u'a', u'b'], dtype='object')
df2.columns
Index([u'col1', u'col2', u'col3'], dtype='object')
# 根據(jù)索引查看數(shù)據(jù) df2.loc['a'] # 索引為a這一行的數(shù)據(jù) # df2.iloc[0] 跟上面的操作等價(jià),一個(gè)是根據(jù)索引名,一個(gè)是根據(jù)數(shù)字索引訪問(wèn)數(shù)據(jù)
col1 1 col2 2 col3 3 Name: a, dtype: int64
print df2.loc[['a','b']] # 訪問(wèn)多行數(shù)據(jù),索引參數(shù)為一個(gè)列表對(duì)象
col1 col2 col3 a 1 2 3 b 4 5 6
print df.loc[df.index[1:3]]
cols b 2 c 3
# 訪問(wèn)列數(shù)據(jù) print df2[['col1','col3']]
col1 col3 a 1 3 b 4 6
計(jì)算
# DataFrame元素求和 # 默認(rèn)是對(duì)每列元素求和 print df2.sum()
col1 5 col2 7 col3 9 dtype: int64
# 行求和 print df2.sum(1)
a 6 b 15 dtype: int64
# 對(duì)每個(gè)元素乘以2 print df2.apply(lambda x:x*2)
col1 col2 col3 a 2 4 6 b 8 10 12
# 對(duì)每個(gè)元素求平方(支持ndarray一樣的向量化操作) print df2**2
col1 col2 col3 a 1 4 9 b 16 25 36
列擴(kuò)充 # 對(duì)DataFrame對(duì)象進(jìn)行列擴(kuò)充 df2['col4'] = ['cnn','rnn'] print df2
col1 col2 col3 col4 a 1 2 3 cnn b 4 5 6 rnn
# 也可以通過(guò)一個(gè)新的DataFrame對(duì)象來(lái)定義一個(gè)新列,索引自動(dòng)對(duì)應(yīng) df2['col5'] = pd.DataFrame(['MachineLearning','DeepLearning'],index=['a','b']) print df2
col1 col2 col3 col4 col5 a 1 2 3 cnn MachineLearning b 4 5 6 rnn DeepLearning
行擴(kuò)充
# 行進(jìn)行擴(kuò)充 print df2.append(pd.DataFrame({'col1':7,'col2':8,'col3':9,'col4':'rcnn','col5':'ReinforcementLearning'},index=['c']))
col1 col2 col3 col4 col5 a 1 2 3 cnn MachineLearning b 4 5 6 rnn DeepLearning c 7 8 9 rcnn ReinforcementLearning
注意!
# 如果在進(jìn)行 行擴(kuò)充時(shí)候沒(méi)有,指定index的參數(shù),索引會(huì)被數(shù)字取代 print df2.append({'col1':10,'col2':11,'col3':12,'col4':'frnn','col5':'DRL'},ignore_index=True)
col1 col2 col3 col4 col5 0 1 2 3 cnn MachineLearning 1 4 5 6 rnn DeepLearning 2 10 11 12 frnn DRL
# 以上的行擴(kuò)充,并沒(méi)有真正修改,df2這個(gè)DataFrame對(duì)象,除非 df2 = df2.append(pd.DataFrame({'col1':7,'col2':8,'col3':9,'col4':'rcnn','col5':'ReinforcementLearning'},index=['c'])) print df2
col1 col2 col3 col4 col5 a 1 2 3 cnn MachineLearning b 4 5 6 rnn DeepLearning c 7 8 9 rcnn ReinforcementLearning c 7 8 9 rcnn ReinforcementLearning
print df2.loc['c']
col1 col2 col3 col4 col5 c 7 8 9 rcnn ReinforcementLearning c 7 8 9 rcnn ReinforcementLearning
DataFrame對(duì)象的合并
# DataFrame 對(duì)象的合并 df_a = pd.DataFrame(['wang','jing','hui','is','a','master'],columns=['col6'],index=['a','b','c','d','e','f']) print df_a
col6 a wang b jing c hui d is e a f master
# 默認(rèn)合并,只保留dfb中的全部索引 dfb = pd.DataFrame([1,2,4,5,6,7],columns=['col1'],index=['a','b','c','d','f','g']) print dfb.join(df_a)
col1 col6 a 1 wang b 2 jing c 4 hui d 5 is f 6 master g 7 NaN
# 默認(rèn)合并之接受索引已經(jīng)存在的值 # 通過(guò)指定參數(shù) how,指定合并的方式 print dfb.join(df_a,how='inner') # 合并兩個(gè)DataFrame對(duì)象的交集
col1 col6 a 1 wang b 2 jing c 4 hui d 5 is f 6 master
# 合并兩個(gè)DataFrame對(duì)象的并集 print dfb.join(df_a,how='outer')
col1 col6 a 1.0 wang b 2.0 jing c 4.0 hui d 5.0 is e NaN a f 6.0 master g 7.0 NaN
以上這篇Pandas:DataFrame對(duì)象的基礎(chǔ)操作方法就是小編分享給大家的全部?jī)?nèi)容了,希望能給大家一個(gè)參考,也希望大家多多支持億速云。
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