剛剛接觸pandas的朋友，想了解數(shù)據(jù)結(jié)構(gòu)，就一定要認(rèn)識(shí)DataFrame，接下來(lái)給大家詳細(xì)介紹！
初識(shí)pandas數(shù)據(jù)結(jié)構(gòu)：DataFrame
import numpy as np import pandas as pd
data = {"name": ["Jack", "Tom", "LiSa"],
"age": [20, 21, 18],
"city": ["BeiJing", "TianJin", "ShenZhen"]}
print(data)
print("")
frame = pd.DataFrame(data) # 創(chuàng)建DataFrame
print(frame)
print("")
print(frame.index) # 查看行索引
print("")
print(frame.columns) # 查看列索引
print("")
print(frame.values) # 查看值
{'name': ['Jack', 'Tom', 'LiSa'], 'age': [20, 21, 18], 'city': ['BeiJing', 'TianJin', 'ShenZhen']}
age city name
0 20 BeiJing Jack
1 21 TianJin Tom
2 18 ShenZhen LiSa
RangeIndex(start=0, stop=3, step=1)
Index(['age', 'city', 'name'], dtype='object')
[[20 'BeiJing' 'Jack']
[21 'TianJin' 'Tom']
[18 'ShenZhen' 'LiSa']]
創(chuàng)建DataFrame
方法一: 由字典創(chuàng)建 字典的key是列索引值可以是
1.列表
2.ndarray
3.Series
# 值是ndarray 注意: 用ndarray創(chuàng)建DataFrame值的個(gè)數(shù)必須相同 否則報(bào)錯(cuò) data2 = {"one": np.random.rand(3), "two": np.random.rand(3) } print(data2) print("") print(pd.DataFrame(data2))
{'one': array([ 0.60720023, 0.30838024, 0.30678266]), 'two': array([ 0.21368784, 0.03797809, 0.41698718])}
one two
0 0.607200 0.213688
1 0.308380 0.037978
2 0.306783 0.416987
# 值是Series--帶有標(biāo)簽的一維數(shù)組 注意: 用Series創(chuàng)建DataFrame值的個(gè)數(shù)可以不同 少的值用Nan填充 data3 = {"one": pd.Series(np.random.rand(4)), "two": pd.Series(np.random.rand(5)) } print(data3) print("") df3 = pd.DataFrame(data3) print(df3) print("")
{'one': 0 0.217639
1 0.921641
2 0.898810
3 0.933510
dtype: float64, 'two': 0 0.132789
1 0.099904
2 0.723495
3 0.719173
4 0.477456
dtype: float64}
one two
0 0.217639 0.132789
1 0.921641 0.099904
2 0.898810 0.723495
3 0.933510 0.719173
4 NaN 0.477456
# 值是Series--帶有標(biāo)簽的一維數(shù)組 注意: 用Series創(chuàng)建DataFrame值的個(gè)數(shù)可以不同 少的值用Nan填充 data3 = {"one": pd.Series(np.random.rand(4)), "two": pd.Series(np.random.rand(5)) } print(data3) print("") df3 = pd.DataFrame(data3) print(df3) print("")
{'one': 0 0.217639
1 0.921641
2 0.898810
3 0.933510
dtype: float64, 'two': 0 0.132789
1 0.099904
2 0.723495
3 0.719173
4 0.477456
dtype: float64}
one two
0 0.217639 0.132789
1 0.921641 0.099904
2 0.898810 0.723495
3 0.933510 0.719173
4 NaN 0.477456
方法二: 通過(guò)二維數(shù)組直接創(chuàng)建
data = [{"one": 1, "two": 2}, {"one": 5, "two": 10, "three": 15}] # 每一個(gè)字典在DataFrame里就是一行數(shù)據(jù)
print(data)
print("")
df1 = pd.DataFrame(data)
print(df1)
print("")
df2 = pd.DataFrame(data, index=list("ab"), columns=["one", "two", "three", "four"])
print(df2)
[{'one': 1, 'two': 2}, {'one': 5, 'two': 10, 'three': 15}]
one three two
0 1 NaN 2
1 5 15.0 10
one two three four
a 1 2 NaN NaN
b 5 10 15.0 NaN
方法三: 由字典組成的列表創(chuàng)建 DataFrame
# columns為字典的key index為子字典的key
data = {"Jack": {"age":1, "country":"China", "sex":"man"},
"LiSa": {"age":18, "country":"America", "sex":"women"},
"Tom": {"age":20, "country":"English"}}
df1 = pd.DataFrame(data)
print(df1)
print("")
# 注意: 這里的index并不能給子字典的key(行索引)重新命名 但可以給子字典的key重新排序 若出現(xiàn)原數(shù)組沒(méi)有的index 那么就填充N(xiāo)aN值
df2 = pd.DataFrame(data, index=["sex", "age", "country"])
print(df2)
print("")
df3 = pd.DataFrame(data, index=list("abc"))
print(df3)
print("")
# columns 給列索引重新排序 若出現(xiàn)原數(shù)組沒(méi)有的列索引填充N(xiāo)aN值
df4 = pd.DataFrame(data, columns=["Tom", "LiSa", "Jack", "TangMu"])
print(df4)
Jack LiSa Tom age 1 18 20 country China America English sex man women NaN Jack LiSa Tom sex man women NaN age 1 18 20 country China America English Jack LiSa Tom a NaN NaN NaN b NaN NaN NaN c NaN NaN NaN Tom LiSa Jack TangMu age 20 18 1 NaN country English America China NaN sex NaN women man NaN
方法四: 由字典組成的字典
# columns為字典的key index為子字典的key
data = {"Jack": {"age":1, "country":"China", "sex":"man"},
"LiSa": {"age":18, "country":"America", "sex":"women"},
"Tom": {"age":20, "country":"English"}}
df1 = pd.DataFrame(data)
print(df1)
print("")
# 注意: 這里的index并不能給子字典的key(行索引)重新命名 但可以給子字典的key重新排序 若出現(xiàn)原數(shù)組沒(méi)有的index 那么就填充N(xiāo)aN值
df2 = pd.DataFrame(data, index=["sex", "age", "country"])
print(df2)
print("")
df3 = pd.DataFrame(data, index=list("abc"))
print(df3)
print("")
# columns 給列索引重新排序 若出現(xiàn)原數(shù)組沒(méi)有的列索引填充N(xiāo)aN值
df4 = pd.DataFrame(data, columns=["Tom", "LiSa", "Jack", "TangMu"])
print(df4)
Jack LiSa Tom age 1 18 20 country China America English sex man women NaN Jack LiSa Tom sex man women NaN age 1 18 20 country China America English Jack LiSa Tom a NaN NaN NaN b NaN NaN NaN c NaN NaN NaN Tom LiSa Jack TangMu age 20 18 1 NaN country English America China NaN sex NaN women man NaN
選擇行與列
選擇列 直接用df["列標(biāo)簽"]
df = pd.DataFrame(np.random.rand(12).reshape(3,4)*100, index = ["one", "two", "three"], columns = ["a", "b", "c", "d"]) print(df) print("") print(df["a"], " ", type(df["a"])) # 取一列 print("") print(df[["a", "c"]], " ", type(df[["a", "c"]])) # 取多列
a b c d one 92.905464 11.630358 19.518051 77.417377 two 91.107357 0.641600 4.913662 65.593182 three 3.152801 42.324671 14.030304 22.138608 one 92.905464 two 91.107357 three 3.152801 Name: a, dtype: float64pandas.core.series.series'=""> a c one 92.905464 19.518051 two 91.107357 4.913662 three 3.152801 14.030304 pandas.core.frame.dataframe'="">
選擇行不能通過(guò)標(biāo)簽索引 df["one"] 來(lái)選擇行 要用 df.loc["one"], loc就是針對(duì)行來(lái)操作的
print(df)
print("")
print(df.loc["one"], " ", type(df.loc["one"])) # 取一行
print("")
print(df.loc[["one", "three"]], " ", type(df.loc[["one", "three"]])) # 取不連續(xù)的多行
print("")
a b c d one 92.905464 11.630358 19.518051 77.417377 two 91.107357 0.641600 4.913662 65.593182 three 3.152801 42.324671 14.030304 22.138608 a 92.905464 b 11.630358 c 19.518051 d 77.417377 Name: one, dtype: float64pandas.core.series.series'=""> a b c d one 92.905464 11.630358 19.518051 77.417377 three 3.152801 42.324671 14.030304 22.138608 pandas.core.frame.dataframe'="">
loc支持切片索引--針對(duì)行 并包含末端 df.loc["one": "three"]
df = pd.DataFrame(np.random.rand(16).reshape(4,4)*100, index=["one", "two", "three", "four"], columns=["a", "b", "c", "d"]) print(df) print("") print(df.loc["one": "three"]) print("") print(df[: 3]) # 切片表示取連續(xù)的多行(盡量不用 免得混淆)
a b c d
one 65.471894 19.137274 31.680635 41.659808
two 31.570587 45.575849 37.739644 5.140845
three 54.930986 68.232707 17.215544 70.765401
four 45.591798 63.274956 74.056045 2.466652
a b c d
one 65.471894 19.137274 31.680635 41.659808
two 31.570587 45.575849 37.739644 5.140845
three 54.930986 68.232707 17.215544 70.765401
a b c d
one 65.471894 19.137274 31.680635 41.659808
two 31.570587 45.575849 37.739644 5.140845
three 54.930986 68.232707 17.215544 70.765401
iloc也是對(duì)行來(lái)操作的 只不過(guò)把行標(biāo)簽改成了行索引 并且是不包含末端的
print(df)
print("")
print(df.iloc[0]) # 取一行
print("")
print(df.iloc[[0,2]]) # 取不連續(xù)的多行
print("")
print(df.iloc[0:3]) # 不包含末端
a b c d
one 65.471894 19.137274 31.680635 41.659808
two 31.570587 45.575849 37.739644 5.140845
three 54.930986 68.232707 17.215544 70.765401
four 45.591798 63.274956 74.056045 2.466652
a 65.471894
b 19.137274
c 31.680635
d 41.659808
Name: one, dtype: float64
a b c d
one 65.471894 19.137274 31.680635 41.659808
three 54.930986 68.232707 17.215544 70.765401
a b c d
one 65.471894 19.137274 31.680635 41.659808
two 31.570587 45.575849 37.739644 5.140845
three 54.930986 68.232707 17.215544 70.765401
布爾型索引
df = pd.DataFrame(np.random.rand(16).reshape(4,4)*100, index=["one", "two", "three", "four"], columns=["a", "b", "c", "d"]) print(df) print("") d1 = df >50 # d1為布爾型索引 print(d1) print("") print(df[d1]) # df根據(jù)d1 只返回True的值 False的值對(duì)應(yīng)為NaN print("")
a b c d
one 91.503673 74.080822 85.274682 80.788609
two 49.670055 42.221393 36.674490 69.272958
three 78.349843 68.090150 22.326223 93.984369
four 79.057146 77.687246 32.304265 0.567816
a b c d
one True True True True
two False False False True
three True True False True
four True True False False
a b c d
one 91.503673 74.080822 85.274682 80.788609
two NaN NaN NaN 69.272958
three 78.349843 68.090150 NaN 93.984369
four 79.057146 77.687246 NaN NaN
選取某一列作為布爾型索引 返回True所在行的所有列 注意: 不能選取多列作為布爾型索引
df = pd.DataFrame(np.random.rand(16).reshape(4,4)*100, index=["one", "two", "three", "four"], columns=["a", "b", "c", "d"], dtype=np.int64) print(df) print("") d2 = df["b"] > 50 print(d2) print("") print(df[d2])
a b c d
one 27 18 47 61
two 26 35 16 78
three 80 98 94 41
four 85 3 47 90
one False
two False
three True
four False
Name: b, dtype: bool
a b c d
three 80 98 94 41
選取多列作為布爾型索引 返回True所對(duì)應(yīng)的值 False對(duì)應(yīng)為NaN 沒(méi)有的列全部填充為NaN
df = pd.DataFrame(np.random.rand(16).reshape(4,4)*100, index=["one", "two", "three", "four"], columns=["a", "b", "c", "d"], dtype=np.int64) print(df) print("") d3 = df[["a", "c"]] > 50 print(d3) print("") print(df[d3])
a b c d
one 49 82 32 39
two 78 2 24 84
three 6 84 84 69
four 21 89 16 77
a c
one False False
two True False
three False True
four False False
a b c d
one NaN NaN NaN NaN
two 78.0 NaN NaN NaN
three NaN NaN 84.0 NaN
four NaN NaN NaN NaN
多重索引
print(df)
a b c d one 49 82 32 39 two 78 2 24 84 three 6 84 84 69 four 21 89 16 77
print(df["a"].loc[["one", "three"]]) # 取列再取行
print("")
print(df[["a", "c"]].iloc[0:3])
one 49
three 6
Name: a, dtype: int64
a c
one 49 32
two 78 24
three 6 84
print(df.loc[["one", "three"]][["a", "c"]]) # 取行再取列
a c one 49 32 three 6 84
print(df > 50)
print("")
print(df[df>50])
print("")
print(df[df>50][["a","b"]])
a b c d
one False True False False
two True False False True
three False True True True
four False True False True
a b c d
one NaN 82.0 NaN NaN
two 78.0 NaN NaN 84.0
three NaN 84.0 84.0 69.0
four NaN 89.0 NaN 77.0
a b
one NaN 82.0
two 78.0 NaN
three NaN 84.0
four NaN 89.0
DataFrame基本技巧
import numpy as np import pandas as pd
arr = np.random.rand(16).reshape(8, 2)*10
# print(arr)
print("")
print(len(arr))
print("")
df = pd.DataFrame(arr, index=[chr(i) for i in range(97, 97+len(arr))], columns=["one", "two"])
print(df)
8
one two
a 2.129959 1.827002
b 8.631212 0.423903
c 6.262012 3.851107
d 6.890305 9.543065
e 6.883742 3.643955
f 2.740878 6.851490
g 6.242513 7.402237
h 9.226572 3.179664
查看數(shù)據(jù)
print(df)
print("")
print(df.head(2)) # 查看頭部數(shù)據(jù) 默認(rèn)查看5條
print("")
print(df.tail(3)) # 查看末尾數(shù)據(jù) 默認(rèn)查看5條
one two
a 2.129959 1.827002
b 8.631212 0.423903
c 6.262012 3.851107
d 6.890305 9.543065
e 6.883742 3.643955
f 2.740878 6.851490
g 6.242513 7.402237
h 9.226572 3.179664
one two
a 2.129959 1.827002
b 8.631212 0.423903
one two
f 2.740878 6.851490
g 6.242513 7.402237
h 9.226572 3.179664
轉(zhuǎn)置
print(df)
one two a 2.129959 1.827002 b 8.631212 0.423903 c 6.262012 3.851107 d 6.890305 9.543065 e 6.883742 3.643955 f 2.740878 6.851490 g 6.242513 7.402237 h 9.226572 3.179664
print(df.T)
a b c d e f g \
one 2.129959 8.631212 6.262012 6.890305 6.883742 2.740878 6.242513
two 1.827002 0.423903 3.851107 9.543065 3.643955 6.851490 7.402237
h
one 9.226572
two 3.179664
添加與修改
df = pd.DataFrame(np.random.rand(16).reshape(4,4),index=["one", "two", "three", "four"], columns=["a", "b", "c", "d"]) print(df) print("") df.loc["five"] = 100 # 增加一行 print(df) print("") df["e"] = 10 # 增加一列 print(df) print("") df["e"] = 101 # 修改一列 print(df) print("") df.loc["five"] = 111 # 修改一行 print(df) print("")
a b c d
one 0.708481 0.285426 0.355058 0.990070
two 0.199559 0.733047 0.322982 0.791169
three 0.198043 0.801163 0.356082 0.857501
four 0.430182 0.020549 0.896011 0.503088
a b c d
one 0.708481 0.285426 0.355058 0.990070
two 0.199559 0.733047 0.322982 0.791169
three 0.198043 0.801163 0.356082 0.857501
four 0.430182 0.020549 0.896011 0.503088
five 100.000000 100.000000 100.000000 100.000000
a b c d e
one 0.708481 0.285426 0.355058 0.990070 10
two 0.199559 0.733047 0.322982 0.791169 10
three 0.198043 0.801163 0.356082 0.857501 10
four 0.430182 0.020549 0.896011 0.503088 10
five 100.000000 100.000000 100.000000 100.000000 10
a b c d e
one 0.708481 0.285426 0.355058 0.990070 101
two 0.199559 0.733047 0.322982 0.791169 101
three 0.198043 0.801163 0.356082 0.857501 101
four 0.430182 0.020549 0.896011 0.503088 101
five 100.000000 100.000000 100.000000 100.000000 101
a b c d e
one 0.708481 0.285426 0.355058 0.990070 101
two 0.199559 0.733047 0.322982 0.791169 101
three 0.198043 0.801163 0.356082 0.857501 101
four 0.430182 0.020549 0.896011 0.503088 101
five 111.000000 111.000000 111.000000 111.000000 111
刪除 del(刪除行)/drop(刪除列 指定axis=1刪除行)
df = pd.DataFrame(np.random.rand(16).reshape(4,4),index=["one", "two", "three", "four"], columns=["a", "b", "c", "d"]) print(df) print("") del df["a"] # 刪除列 改變?cè)瓟?shù)組 print(df)
a b c d
one 0.339979 0.577661 0.108308 0.482164
two 0.374043 0.102067 0.660970 0.786986
three 0.384832 0.076563 0.529472 0.358780
four 0.938592 0.852895 0.466709 0.938307
b c d
one 0.577661 0.108308 0.482164
two 0.102067 0.660970 0.786986
three 0.076563 0.529472 0.358780
four 0.852895 0.466709 0.938307
df = pd.DataFrame(np.random.rand(16).reshape(4,4),index=["one", "two", "three", "four"], columns=["a", "b", "c", "d"]) print(df) print("") d1 = df.drop("one") # 刪除行 并返回新的數(shù)組 不改變?cè)瓟?shù)組 print(d1) print("") print(df)
a b c d
one 0.205438 0.324132 0.401131 0.368300
two 0.471426 0.671785 0.837956 0.097416
three 0.888816 0.451950 0.137032 0.568844
four 0.524813 0.448306 0.875787 0.479477
a b c d
two 0.471426 0.671785 0.837956 0.097416
three 0.888816 0.451950 0.137032 0.568844
four 0.524813 0.448306 0.875787 0.479477
a b c d
one 0.205438 0.324132 0.401131 0.368300
two 0.471426 0.671785 0.837956 0.097416
three 0.888816 0.451950 0.137032 0.568844
four 0.524813 0.448306 0.875787 0.479477
df = pd.DataFrame(np.random.rand(16).reshape(4,4),index=["one", "two", "three", "four"], columns=["a", "b", "c", "d"]) print(df) print("") d2 = df.drop("a", axis=1) # 刪除列 返回新的數(shù)組 不會(huì)改變?cè)瓟?shù)組 print(d2) print("") print(df)
a b c d
one 0.939552 0.613218 0.357056 0.534264
two 0.110583 0.602123 0.990186 0.149132
three 0.756016 0.897848 0.176100 0.204789
four 0.655573 0.819009 0.094322 0.656406
b c d
one 0.613218 0.357056 0.534264
two 0.602123 0.990186 0.149132
three 0.897848 0.176100 0.204789
four 0.819009 0.094322 0.656406
a b c d
one 0.939552 0.613218 0.357056 0.534264
two 0.110583 0.602123 0.990186 0.149132
three 0.756016 0.897848 0.176100 0.204789
four 0.655573 0.819009 0.094322 0.656406
排序
根據(jù)指定列的列值排序 同時(shí)列值所在的行也會(huì)跟著移動(dòng) .sort_values(['列'])
# 單列 df = pd.DataFrame(np.random.rand(16).reshape(4,4), columns=["a", "b", "c", "d"]) print(df) print("") print(df.sort_values(['a'])) # 默認(rèn)升序 print("") print(df.sort_values(['a'], ascending=False)) # 降序
a b c d
0 0.616386 0.416094 0.072445 0.140167
1 0.263227 0.079205 0.520708 0.866316
2 0.665673 0.836688 0.733966 0.310229
3 0.405777 0.090530 0.991211 0.712312
a b c d
1 0.263227 0.079205 0.520708 0.866316
3 0.405777 0.090530 0.991211 0.712312
0 0.616386 0.416094 0.072445 0.140167
2 0.665673 0.836688 0.733966 0.310229
a b c d
2 0.665673 0.836688 0.733966 0.310229
0 0.616386 0.416094 0.072445 0.140167
3 0.405777 0.090530 0.991211 0.712312
1 0.263227 0.079205 0.520708 0.866316
根據(jù)索引排序 .sort_index()
df = pd.DataFrame(np.random.rand(16).reshape(4,4), index=[2,1,3,0], columns=["a", "b", "c", "d"]) print(df) print("") print(df.sort_index()) # 默認(rèn)升序 print("") print(df.sort_index(ascending=False)) # 降序
a b c d
2 0.669311 0.118176 0.635512 0.248388
1 0.752321 0.935779 0.572554 0.274019
3 0.701334 0.354684 0.592998 0.402686
0 0.548317 0.966295 0.191219 0.307908
a b c d
0 0.548317 0.966295 0.191219 0.307908
1 0.752321 0.935779 0.572554 0.274019
2 0.669311 0.118176 0.635512 0.248388
3 0.701334 0.354684 0.592998 0.402686
a b c d
3 0.701334 0.354684 0.592998 0.402686
2 0.669311 0.118176 0.635512 0.248388
1 0.752321 0.935779 0.572554 0.274019
0 0.548317 0.966295 0.191219 0.307908
df = pd.DataFrame(np.random.rand(16).reshape(4,4), index=["x", "z", "y", "t"], columns=["a", "b", "c", "d"]) print(df) print("") print(df.sort_index()) # 根據(jù)字母順序表排序
a b c d
x 0.717421 0.206383 0.757656 0.720580
z 0.969988 0.551812 0.210200 0.083031
y 0.956637 0.759216 0.350744 0.335287
t 0.846718 0.207411 0.936231 0.891330
a b c d
t 0.846718 0.207411 0.936231 0.891330
x 0.717421 0.206383 0.757656 0.720580
y 0.956637 0.759216 0.350744 0.335287
z 0.969988 0.551812 0.210200 0.083031
df = pd.DataFrame(np.random.rand(16).reshape(4,4), index=["three", "one", "four", "two"], columns=["a", "b", "c", "d"]) print(df) print("") print(df.sort_index()) # 根據(jù)單詞首字母排序
a b c d
three 0.173818 0.902347 0.106037 0.303450
one 0.591793 0.526785 0.101916 0.884698
four 0.685250 0.364044 0.932338 0.668774
two 0.240763 0.260322 0.722891 0.634825
a b c d
four 0.685250 0.364044 0.932338 0.668774
one 0.591793 0.526785 0.101916 0.884698
three 0.173818 0.902347 0.106037 0.303450
two 0.240763 0.260322 0.722891 0.634825
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