剛剛接觸pandas的朋友，想了解數據結構，就一定要認識DataFrame，接下來給大家詳細介紹！
初識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']]
創建DataFrame
方法一: 由字典創建 字典的key是列索引 值可以是1.列表 2.ndarray 3.Series
# 值是列表
data1 = {"a": [1,2,3],
"b": [4,5,6],
"c": [7,8,9]
}
print(data1)
print("")
print(pd.DataFrame(data1)) # 創建DataFrame
print("")
# 注意: index是可以給行索引重新命名 columns是給列索引重新指定順序 如果沒有該列 那麼產生NaN值
print(pd.DataFrame(data1, index=list("mnp"), columns=list("bcad")))
print("")
{'a': [1, 2, 3], 'b': [4, 5, 6], 'c': [7, 8, 9]}
a b c
0 1 4 7
1 2 5 8
2 3 6 9
b c a d
m 4 7 1 NaN
n 5 8 2 NaN
p 6 9 3 NaN
# 值是ndarray 注意: 用ndarray創建DataFrame值的個數必須相同 否則報錯
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--帶有標籤的一維數組 注意: 用Series創建DataFrame值的個數可以不同 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: 通過二維數組直接創建
arr = np.random.rand(12).reshape(3,4)
print(arr)
print("")
df1 = pd.DataFrame(arr)
print(df1)
print("")
df2 = pd.DataFrame(arr, index=list("abc"), columns=["one", "two", "three", "four"]) # 通過index和columns指定行索引和列索引
print(df2)
[[ 0.85898536 0.40300549 0.80043098 0.87045042]
[ 0.56918302 0.34589982 0.63733905 0.93012927]
[ 0.47535281 0.93652147 0.57446896 0.59470213]]
0 1 2 3
0 0.858985 0.403005 0.800431 0.870450
1 0.569183 0.345900 0.637339 0.930129
2 0.475353 0.936521 0.574469 0.594702
one two three four
a 0.858985 0.403005 0.800431 0.870450
b 0.569183 0.345900 0.637339 0.930129
c 0.475353 0.936521 0.574469 0.594702
方法三: 由字典組成的列表創建 DataFrame
data = [{"one": 1, "two": 2}, {"one": 5, "two": 10, "three": 15}] # 每一個字典在DataFrame裡就是一行數據
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
創建方法四: 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: 這裡的index並不能給子字典的key(行索引)重新命名 但可以給子字典的key重新排序 若出現原數組沒有的index 那麼就填充NaN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 若出現原數組沒有的列索引填充NaN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
DataFrame索引
選擇行與列
選擇列 直接用df["列標籤"]
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: float64 <class 'pandas.core.series.Series'>
a c
one 92.905464 19.518051
two 91.107357 4.913662
three 3.152801 14.030304 <class 'pandas.core.frame.DataFrame'>
選擇行 不能通過標籤索引 df["one"] 來選擇行 要用 df.loc["one"], loc就是針對行來操作的
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: float64 <class 'pandas.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 <class 'pandas.core.frame.DataFrame'>
loc支持切片索引--針對行 並包含末端 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]) # 切片表示取連續的多行(儘量不用 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也是對行來操作的 只不過把行標籤改成了行索引 並且是不包含末端的
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d1 只返回True的值 False的值對應為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所對應的值 False對應為NaN 沒有的列全部填充為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
查看數據
print(df)
print("")
print(df.head(2)) # 查看頭部數據 默認查看5條
print("")
print(df.tail(3)) # 查看末尾數據 默認查看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 改變原數組
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 並返回新的數組 不改變原數組
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 返回新的數組 不會改變原數組
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
根據指定列的列值排序 同時列值所在的行也會跟著移動 .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