刚刚接触pandas的朋友，想了解数据结构，就一定要认识DataFrame，接下来给大家详细介绍！
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) # 创建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']]
方法一: 由字典创建 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创建DataFrame值的个数必须相同 否则报错 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--带有标签的一维数组 注意: 用Series创建DataFrame值的个数可以不同 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--带有标签的一维数组 注意: 用Series创建DataFrame值的个数可以不同 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: 通过二维数组直接创建
data = [{"one": 1, "two": 2}, {"one": 5, "two": 10, "three": 15}] # 每一个字典在DataFrame里就是一行数据
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: 由字典组成的列表创建 DataFrame
# columns为字典的key index为子字典的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: 这里的index并不能给子字典的key(行索引)重新命名 但可以给子字典的key重新排序 若出现原数组没有的index 那么就填充NaN值
df2 = pd.DataFrame(data, index=["sex", "age", "country"])
print(df2)
print("")
df3 = pd.DataFrame(data, index=list("abc"))
print(df3)
print("")
# columns 给列索引重新排序 若出现原数组没有的列索引填充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
方法四: 由字典组成的字典
# columns为字典的key index为子字典的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: 这里的index并不能给子字典的key(行索引)重新命名 但可以给子字典的key重新排序 若出现原数组没有的index 那么就填充NaN值
df2 = pd.DataFrame(data, index=["sex", "age", "country"])
print(df2)
print("")
df3 = pd.DataFrame(data, index=list("abc"))
print(df3)
print("")
# columns 给列索引重新排序 若出现原数组没有的列索引填充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
选择行与列
选择列 直接用df["列标签"]
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'="">
选择行不能通过标签索引 df["one"] 来选择行 要用 df.loc["one"], loc就是针对行来操作的
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"]])) # 取不连续的多行
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--针对行 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]) # 切片表示取连续的多行(尽量不用 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也是对行来操作的 只不过把行标签改成了行索引 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]]) # 取不连续的多行
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
布尔型索引
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为布尔型索引 print(d1) print("") print(df[d1]) # df根据d1 只返回True的值 False的值对应为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
选取某一列作为布尔型索引 返回True所在行的所有列 注意: 不能选取多列作为布尔型索引
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
选取多列作为布尔型索引 返回True所对应的值 False对应为NaN 没有的列全部填充为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
查看数据
print(df)
print("")
print(df.head(2)) # 查看头部数据 默认查看5条
print("")
print(df.tail(3)) # 查看末尾数据 默认查看5条
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
转置
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
添加与修改
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
删除 del(删除行)/drop(删除列 指定axis=1删除行)
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"] # 删除列 改变原数组 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") # 删除行 并返回新的数组 不改变原数组 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) # 删除列 返回新的数组 不会改变原数组 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
根据指定列的列值排序 同时列值所在的行也会跟着移动 .sort_values(['列'])
# 单列 df = pd.DataFrame(np.random.rand(16).reshape(4,4), columns=["a", "b", "c", "d"]) print(df) print("") print(df.sort_values(['a'])) # 默认升序 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
根据索引排序 .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()) # 默认升序 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()) # 根据字母顺序表排序
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()) # 根据单词首字母排序
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
数据分析咨询请扫描二维码
若不方便扫码，搜微信号：CDAshujufenxi
R 语言：数据科学与科研领域的核心工具及优势解析 一、引言 在数据驱动决策的时代，无论是科研人员验证实验假设（如前文中的 T ...
2025-09-08T 检验在假设检验中的应用与实践 一、引言 在科研数据分析、医学实验验证、经济指标对比等领域，常常需要判断 “样本间的差异是 ...
2025-09-08在商业竞争日益激烈的当下，“用数据说话” 已从企业的 “加分项” 变为 “生存必需”。然而，零散的数据分析无法持续为业务赋能 ...
2025-09-08随机森林算法的核心特点：原理、优势与应用解析 在机器学习领域，随机森林（Random Forest）作为集成学习（Ensemble Learning） ...
2025-09-05Excel 区域名定义：从基础到进阶的高效应用指南 在 Excel 数据处理中，频繁引用单元格区域（如A2:A100、B3:D20）不仅容易出错， ...
2025-09-05CDA 数据分析师：以六大分析方法构建数据驱动业务的核心能力 在数据驱动决策成为企业共识的当下，CDA（Certified Data Analyst） ...
2025-09-05SQL 日期截取：从基础方法到业务实战的全维度解析 在数据处理与业务分析中，日期数据是连接 “业务行为” 与 “时间维度” 的核 ...
2025-09-04在卷积神经网络（CNN）的发展历程中，解决 “梯度消失”“特征复用不足”“模型参数冗余” 一直是核心命题。2017 年提出的密集连 ...
2025-09-04CDA 数据分析师：驾驭数据范式，释放数据价值 在数字化转型浪潮席卷全球的当下，数据已成为企业核心生产要素。而 CDA（Certified ...
2025-09-04K-Means 聚类：无监督学习中数据分群的核心算法 在数据分析领域，当我们面对海量无标签数据（如用户行为记录、商品属性数据、图 ...
2025-09-03特征值、特征向量与主成分：数据降维背后的线性代数逻辑 在机器学习、数据分析与信号处理领域，“降维” 是破解高维数据复杂性的 ...
2025-09-03CDA 数据分析师与数据分析：解锁数据价值的关键 在数字经济高速发展的今天，数据已成为企业核心资产与社会发展的重要驱动力。无 ...
2025-09-03解析 loss.backward ()：深度学习中梯度汇总与同步的自动触发核心 在深度学习模型训练流程中，loss.backward()是连接 “前向计算 ...
2025-09-02要解答 “画 K-S 图时横轴是等距还是等频” 的问题，需先明确 K-S 图的核心用途（检验样本分布与理论分布的一致性），再结合横轴 ...
2025-09-02CDA 数据分析师：助力企业破解数据需求与数据分析需求难题 在数字化浪潮席卷全球的当下，数据已成为企业核心战略资产。无论是市 ...
2025-09-02Power BI 度量值实战：基于每月收入与税金占比计算累计税金分摊金额 在企业财务分析中，税金分摊是成本核算与利润统计的核心环节 ...
2025-09-01巧用 ALTER TABLE rent ADD INDEX：租房系统数据库性能优化实践 在租房管理系统中，rent表是核心业务表之一，通常存储租赁订单信 ...
2025-09-01CDA 数据分析师：企业数字化转型的核心引擎 —— 从能力落地到价值跃迁 当数字化转型从 “选择题” 变为企业生存的 “必答题”， ...
2025-09-01数据清洗工具全景指南：从入门到进阶的实操路径 在数据驱动决策的链条中，“数据清洗” 是决定后续分析与建模有效性的 “第一道 ...
2025-08-29机器学习中的参数优化：以预测结果为核心的闭环调优路径 在机器学习模型落地中，“参数” 是连接 “数据” 与 “预测结果” 的关 ...
2025-08-29
京公网安备 11010802034615号
经营许可证编号：京B2-20210330
