【Python】推薦20個(gè)好用到爆的Pandas函數(shù)方法

• items()方法
• iterrows()方法
• insert()方法
• assign()方法
• eval()方法
• pop()方法
• truncate()方法
• count()方法
• add_prefix()方法/add_suffix()方法
• clip()方法
• filter()方法
• first()方法
• isin()方法
• df.plot.area()方法
• df.plot.bar()方法
• df.plot.box()方法
• df.plot.pie()方法

items()方法

df?=?pd.DataFrame({'species':?['bear',?'bear',?'marsupial'],
??????????????????'population':?[1864,?22000,?80000]},
??????????????????index=['panda',?'polar',?'koala'])
df

output

?????????species??population
panda???????bear????????1864
polar???????bear???????22000
koala??marsupial???????80000

然后我們使用items()方法

for?label,?content?in?df.items():
????print(f'label:?{label}')
????print(f'content:?{content}',?sep='\n')
????print("="?*?50)

output

label:?species
content:?panda?????????bear
polar?????????bear
koala????marsupial
Name:?species,?dtype:?object
==================================================
label:?population
content:?panda?????1864
polar????22000
koala????80000
Name:?population,?dtype:?int64
==================================================

相繼的打印出了‘species’和‘population’這兩列的列名和相應(yīng)的內(nèi)容

iterrows()方法

for?label,?content?in?df.iterrows():
????print(f'label:?{label}')
????print(f'content:?{content}',?sep='\n')
????print("="?*?50)

output

label:?panda
content:?species???????bear
population????1864
Name:?panda,?dtype:?object
==================================================
label:?polar
content:?species????????bear
population????22000
Name:?polar,?dtype:?object
==================================================
label:?koala
content:?species???????marsupial
population????????80000
Name:?koala,?dtype:?object
==================================================

insert()方法

insert()方法主要是用于在數(shù)據(jù)集當(dāng)中的特定位置處插入數(shù)據(jù)，示例如下

df.insert(1,?"size",?[2000,?3000,?4000])

output

?????????species??size??population
panda???????bear??2000????????1864
polar???????bear??3000???????22000
koala??marsupial??4000???????80000

可見(jiàn)在DataFrame數(shù)據(jù)集當(dāng)中，列的索引也是從0開(kāi)始的

assign()方法

assign()方法可以用來(lái)在數(shù)據(jù)集當(dāng)中添加新的列，示例如下

df.assign(size_1=lambda?x:?x.population?*?9?/?5?+?32)

output

?????????species??population????size_1
panda???????bear????????1864????3387.2
polar???????bear???????22000???39632.0
koala??marsupial???????80000??144032.0

df.assign(size_1?=?lambda?x:?x.population?*?9?/?5?+?32,
??????????size_2?=?lambda?x:?x.population?*?8?/?5?+?10)

output

?????????species??population????size_1????size_2
panda???????bear????????1864????3387.2????2992.4
polar???????bear???????22000???39632.0???35210.0
koala??marsupial???????80000??144032.0??128010.0

eval()方法

eval()方法主要是用來(lái)執(zhí)行用字符串來(lái)表示的運(yùn)算過(guò)程的，例如

df.eval("size_3?=?size_1?+?size_2")

output

?????????species??population????size_1????size_2????size_3
panda???????bear????????1864????3387.2????2992.4????6379.6
polar???????bear???????22000???39632.0???35210.0???74842.0
koala??marsupial???????80000??144032.0??128010.0??272042.0

當(dāng)然我們也可以同時(shí)對(duì)執(zhí)行多個(gè)運(yùn)算過(guò)程

df?=?df.eval('''
size_3?=?size_1?+?size_2
size_4?=?size_1?-?size_2
''')

output

?????????species??population????size_1????size_2????size_3???size_4
panda???????bear????????1864????3387.2????2992.4????6379.6????394.8
polar???????bear???????22000???39632.0???35210.0???74842.0???4422.0
koala??marsupial???????80000??144032.0??128010.0??272042.0??16022.0

pop()方法

pop()方法主要是用來(lái)刪除掉數(shù)據(jù)集中特定的某一列數(shù)據(jù)

df.pop("size_3")

output

panda??????6379.6
polar?????74842.0
koala????272042.0
Name:?size_3,?dtype:?float64

而原先的數(shù)據(jù)集當(dāng)中就沒(méi)有這個(gè)‘size_3’這一例的數(shù)據(jù)了

truncate()方法

truncate()方法主要是根據(jù)行索引來(lái)篩選指定行的數(shù)據(jù)的，示例如下

df?=?pd.DataFrame({'A':?['a',?'b',?'c',?'d',?'e'],
???????????????????'B':?['f',?'g',?'h',?'i',?'j'],
???????????????????'C':?['k',?'l',?'m',?'n',?'o']},
??????????????????index=[1,?2,?3,?4,?5])

output

???A??B??C
1??a??f??k
2??b??g??l
3??c??h??m
4??d??i??n
5??e??j??o

我們使用truncate()方法來(lái)做一下嘗試

df.truncate(before=2,?after=4)

output

???A??B??C
2??b??g??l
3??c??h??m
4??d??i??n

count()方法

count()方法主要是用來(lái)計(jì)算某一列當(dāng)中非空值的個(gè)數(shù)，示例如下

df?=?pd.DataFrame({"Name":?["John",?"Myla",?"Lewis",?"John",?"John"],
???????????????????"Age":?[24.,?np.nan,?25,?33,?26],
???????????????????"Single":?[True,?True,?np.nan,?True,?False]})

output

????Name???Age?Single
0???John??24.0???True
1???Myla???NaN???True
2??Lewis??25.0????NaN
3???John??33.0???True
4???John??26.0??False

我們使用count()方法來(lái)計(jì)算一下數(shù)據(jù)集當(dāng)中非空值的個(gè)數(shù)

df.count()

output

Name??????5
Age???????4
Single????4
dtype:?int64

add_prefix()方法/add_suffix()方法

s?=?pd.Series([1,?2,?3,?4])

output

0??? 1
1????2
2????3
3????4
dtype:?int64

我們使用add_prefix()方法與add_suffix()方法在Series()數(shù)據(jù)集上

s.add_prefix('row_')

output

row_0????1
row_1????2
row_2????3
row_3????4
dtype:?int64

又例如

s.add_suffix('_row')

output

0_row????1
1_row????2
2_row????3
3_row????4
dtype:?int64

df?=?pd.DataFrame({'A':?[1,?2,?3,?4],?'B':?[3,?4,?5,?6]})

output

???A??B
0??1??3
1??2??4
2??3??5
3??4??6

示例如下

df.add_prefix("column_")

output

???column_A??column_B
0?????????1?????????3
1?????????2?????????4
2?????????3?????????5
3?????????4?????????6

又例如

df.add_suffix("_column")

output

???A_column??B_column
0?????????1?????????3
1?????????2?????????4
2?????????3?????????5
3?????????4?????????6

clip()方法

data?=?{'col_0':?[9,?-3,?0,?-1,?5],?'col_1':?[-2,?-7,?6,?8,?-5]}
df?=?pd.DataFrame(data)

output

df.clip(lower?=?-4,?upper?=?4)

output

???col_0??col_1
0??????4?????-2
1?????-3?????-4
2??????0??????4
3?????-1??????4
4??????4?????-4

filter()方法

pandas當(dāng)中的filter()方法是用來(lái)篩選出特定范圍的數(shù)據(jù)的，示例如下

df?=?pd.DataFrame(np.array(([1,?2,?3],?[4,?5,?6],?[7,?8,?9],?[10,?11,?12])),
??????????????????index=['A',?'B',?'C',?'D'],
??????????????????columns=['one',?'two',?'three'])

output

???one??two??three
A????1????2??????3
B????4????5??????6
C????7????8??????9
D???10???11?????12

我們使用filter()方法來(lái)篩選數(shù)據(jù)

df.filter(items=['one',?'three'])

output

???one??three
A????1??????3
B????4??????6
C????7??????9
D???10?????12

我們還可以使用正則表達(dá)式來(lái)篩選數(shù)據(jù)

df.filter(regex='e$',?axis=1)

output

???one??three
A????1??????3
B????4??????6
C????7??????9
D???10?????12

當(dāng)然通過(guò)參數(shù)axis來(lái)調(diào)整篩選行方向或者是列方向的數(shù)據(jù)

df.filter(like='B',?axis=0)

output

???one??two??three
B????4????5??????6

first()方法

當(dāng)數(shù)據(jù)集當(dāng)中的行索引是日期的時(shí)候，可以通過(guò)該方法來(lái)篩選前面幾行的數(shù)據(jù)

index_1?=?pd.date_range('2021-11-11',?periods=5,?freq='2D')
ts?=?pd.DataFrame({'A':?[1,?2,?3,?4,?5]},?index=index_1)
ts

output

????????????A
2021-11-11??1
2021-11-13??2
2021-11-15??3
2021-11-17??4
2021-11-19??5

我們使用first()方法來(lái)進(jìn)行一些操作，例如篩選出前面3天的數(shù)據(jù)

ts.first('3D')

output

????????????A
2021-11-11??1
2021-11-13??2

isin()方法

isin()方法主要是用來(lái)確認(rèn)數(shù)據(jù)集當(dāng)中的數(shù)值是否被包含在給定的列表當(dāng)中

df?=?pd.DataFrame(np.array(([1,?2,?3],?[4,?5,?6],?[7,?8,?9],?[10,?11,?12])),
??????????????????index=['A',?'B',?'C',?'D'],
??????????????????columns=['one',?'two',?'three'])
df.isin([3,?5,?12])

output

?????one????two??three
A??False??False???True
B??False???True??False
C??False??False??False
D??False??False???True

df.plot.area()方法

df?=?pd.DataFrame({
????'sales':?[30,?20,?38,?95,?106,?65],
????'signups':?[7,?9,?6,?12,?18,?13],
????'visits':?[20,?42,?28,?62,?81,?50],
},?index=pd.date_range(start='2021/01/01',?end='2021/07/01',?freq='M'))

ax?=?df.plot.area(figsize?=?(10,?5))

output

df.plot.bar()方法

下面我們看一下如何通過(guò)一行代碼來(lái)繪制柱狀圖

df?=?pd.DataFrame({'label':['A',?'B',?'C',?'D'],?'values':[10,?30,?50,?70]})
ax?=?df.plot.bar(x='label',?y='values',?rot=20)

output

當(dāng)然我們也可以根據(jù)不同的類(lèi)別來(lái)繪制柱狀圖

age?=?[0.1,?17.5,?40,?48,?52,?69,?88]
weight?=?[2,?8,?70,?1.5,?25,?12,?28]
index?=?['A',?'B',?'C',?'D',?'E',?'F',?'G']
df?=?pd.DataFrame({'age':?age,?'weight':?weight},?index=index)
ax?=?df.plot.bar(rot=0)

output

當(dāng)然我們也可以橫向來(lái)繪制圖表

ax?=?df.plot.barh(rot=0)

output

df.plot.box()方法

我們來(lái)看一下箱型圖的具體的繪制，通過(guò)pandas一行代碼來(lái)實(shí)現(xiàn)

data?=?np.random.randn(25,?3)
df?=?pd.DataFrame(data,?columns=list('ABC'))
ax?=?df.plot.box()

output

df.plot.pie()方法

接下來(lái)是餅圖的繪制

df?=?pd.DataFrame({'mass':?[1.33,?4.87?,?5.97],
???????????????????'radius':?[2439.7,?6051.8,?6378.1]},
??????????????????index=['Mercury',?'Venus',?'Earth'])
plot?=?df.plot.pie(y='mass',?figsize=(8,?8))

output