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
你是否被统计学复杂的理论和晦涩的公式劝退过？别担心，“山有木兮：统计学极简入门（Python）” 将为你一一化解这些难题。课程 ...
2025-03-31在电商、零售、甚至内容付费业务中，你真的了解你的客户吗？ 有些客户下了一两次单就消失了，有些人每个月都回购，有些人曾经是 ...
2025-03-31在数字化浪潮中，数据驱动决策已成为企业发展的核心竞争力，数据分析人才的需求持续飙升。世界经济论坛发布的《未来就业报告》， ...
2025-03-28你有没有遇到过这样的情况？流量进来了，转化率却不高，辛辛苦苦拉来的用户，最后大部分都悄无声息地离开了，这时候漏斗分析就非 ...
2025-03-27TensorFlow Datasets（TFDS）是一个用于下载、管理和预处理机器学习数据集的库。它提供了易于使用的API，允许用户从现有集合中 ...
2025-03-26"不谋全局者，不足谋一域。"在数据驱动的商业时代，战略级数据分析能力已成为职场核心竞争力。《CDA二级教材：商业策略数据分析 ...
2025-03-26当你在某宝刷到【猜你喜欢】时，当抖音精准推来你的梦中情猫时，当美团外卖弹窗刚好是你想吃的火锅店…… 恭喜你，你正在被用户 ...
2025-03-26当面试官问起随机森林时，他到底在考察什么？ ""请解释随机森林的原理""——这是数据分析岗位面试中的经典问题。但你可能不知道 ...
2025-03-25在数字化浪潮席卷的当下，数据俨然成为企业的命脉，贯穿于业务运作的各个环节。从线上到线下，从平台的交易数据，到门店的运营 ...
2025-03-25在互联网和移动应用领域，DAU（日活跃用户数）是一个耳熟能详的指标。无论是产品经理、运营，还是数据分析师，DAU都是衡量产品 ...
2025-03-24ABtest做的好，产品优化效果差不了！可见ABtest在评估优化策略的效果方面地位还是很高的，那么如何在业务中应用ABtest? 结合企业 ...
2025-03-21在企业数据分析中，指标体系是至关重要的工具。不仅帮助企业统一数据标准、提升数据质量，还能为业务决策提供有力支持。本文将围 ...
2025-03-20解锁数据分析师高薪密码，CDA 脱产就业班助你逆袭！ 在数字化浪潮中，数据驱动决策已成为企业发展的核心竞争力，数据分析人才的 ...
2025-03-19在 MySQL 数据库中，查询一张表但是不包含某个字段可以通过以下两种方法实现：使用 SELECT 子句以明确指定想要的字段，或者使 ...
2025-03-17在当今数字化时代，数据成为企业发展的关键驱动力，而用户画像作为数据分析的重要成果，改变了企业理解用户、开展业务的方式。无 ...
2025-03-172025年是智能体（AI Agent）的元年，大模型和智能体的发展比较迅猛。感觉年初的deepseek刚火没多久，这几天Manus又成为媒体头条 ...
2025-03-14以下的文章内容来源于柯家媛老师的专栏，如果您想阅读专栏《小白必备的数据思维课》，点击下方链接 https://edu.cda.cn/goods/sh ...
2025-03-13以下的文章内容来源于刘静老师的专栏，如果您想阅读专栏《10大业务分析模型突破业务瓶颈》，点击下方链接 https://edu.cda.cn/go ...
2025-03-12以下的文章内容来源于柯家媛老师的专栏，如果您想阅读专栏《小白必备的数据思维课》，点击下方链接 https://edu.cda.cn/goods/sh ...
2025-03-11随着数字化转型的加速，企业积累了海量数据，如何从这些数据中挖掘有价值的信息，成为企业提升竞争力的关键。CDA认证考试体系应 ...
2025-03-10
