公眾號:尤而小屋
作者:Peter
編輯:Peter
大家好骤竹,我是Peter~
從26個字母中精選出23個Pandas常用的函數(shù)乒验,將它們的使用方法介紹給大家。其中o电媳、y踏揣、z沒有相應(yīng)的函數(shù)。
import pandas as pd
import numpy as np
下面介紹每個函數(shù)的使用方法匾乓,更多詳細的內(nèi)容請移步官網(wǎng):https://pandas.pydata.org/docs/reference/general_functions.html
assign函數(shù)
df = pd.DataFrame({
'temp_c': [17.0, 25.0]},
index=['Portland', 'Berkeley'])
df
<div>
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<table border="1" class="dataframe">
<thead>
<tr style="text-align: right;">
<th></th>
<th>temp_c</th>
</tr>
</thead>
<tbody>
<tr>
<th>Portland</th>
<td>17.0</td>
</tr>
<tr>
<th>Berkeley</th>
<td>25.0</td>
</tr>
</tbody>
</table>
</div>
# 生成新的字段
df.assign(temp_f=df['temp_c'] * 9 / 5 + 32)
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<table border="1" class="dataframe">
<thead>
<tr style="text-align: right;">
<th></th>
<th>temp_c</th>
<th>temp_f</th>
</tr>
</thead>
<tbody>
<tr>
<th>Portland</th>
<td>17.0</td>
<td>62.6</td>
</tr>
<tr>
<th>Berkeley</th>
<td>25.0</td>
<td>77.0</td>
</tr>
</tbody>
</table>
</div>
df # 原來DataFrame是不改變的
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<table border="1" class="dataframe">
<thead>
<tr style="text-align: right;">
<th></th>
<th>temp_c</th>
</tr>
</thead>
<tbody>
<tr>
<th>Portland</th>
<td>17.0</td>
</tr>
<tr>
<th>Berkeley</th>
<td>25.0</td>
</tr>
</tbody>
</table>
</div>
df["temp_f1"] = df["temp_c"] * 9 / 5 + 32
df
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<style scoped>
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</style>
<table border="1" class="dataframe">
<thead>
<tr style="text-align: right;">
<th></th>
<th>temp_c</th>
<th>temp_f1</th>
</tr>
</thead>
<tbody>
<tr>
<th>Portland</th>
<td>17.0</td>
<td>62.6</td>
</tr>
<tr>
<th>Berkeley</th>
<td>25.0</td>
<td>77.0</td>
</tr>
</tbody>
</table>
</div>
df
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<style scoped>
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</style>
<table border="1" class="dataframe">
<thead>
<tr style="text-align: right;">
<th></th>
<th>temp_c</th>
<th>temp_f1</th>
</tr>
</thead>
<tbody>
<tr>
<th>Portland</th>
<td>17.0</td>
<td>62.6</td>
</tr>
<tr>
<th>Berkeley</th>
<td>25.0</td>
<td>77.0</td>
</tr>
</tbody>
</table>
</div>
bool函數(shù)
返回單個Series或者DataFrame中單個元素的bool值:True或者False
pd.Series([True]).bool()
True
pd.Series([False]).bool()
False
pd.DataFrame({'col': [True]}).bool()
True
pd.DataFrame({'col': [False]}).bool()
False
# # 多個元素引發(fā)報錯
# pd.DataFrame({'col': [True,False]}).bool()
concat函數(shù)
該函數(shù)是用來表示多個DataFrame的拼接捞稿,橫向或者縱向皆可。
df1 = pd.DataFrame({
"sid":["s1","s2"],
"name":["xiaoming","Mike"]})
df1
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</style>
<table border="1" class="dataframe">
<thead>
<tr style="text-align: right;">
<th></th>
<th>sid</th>
<th>name</th>
</tr>
</thead>
<tbody>
<tr>
<th>0</th>
<td>s1</td>
<td>xiaoming</td>
</tr>
<tr>
<th>1</th>
<td>s2</td>
<td>Mike</td>
</tr>
</tbody>
</table>
</div>
df2 = pd.DataFrame({
"sid":["s3","s4"],
"name":["Tom","Peter"]})
df2
<div>
<style scoped>
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vertical-align: middle;
}
.dataframe tbody tr th {
vertical-align: top;
}
.dataframe thead th {
text-align: right;
}
</style>
<table border="1" class="dataframe">
<thead>
<tr style="text-align: right;">
<th></th>
<th>sid</th>
<th>name</th>
</tr>
</thead>
<tbody>
<tr>
<th>0</th>
<td>s3</td>
<td>Tom</td>
</tr>
<tr>
<th>1</th>
<td>s4</td>
<td>Peter</td>
</tr>
</tbody>
</table>
</div>
df3 = pd.DataFrame({
"address":["北京","深圳"],
"sex":["Male","Female"]})
df3
<div>
<style scoped>
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.dataframe tbody tr th {
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.dataframe thead th {
text-align: right;
}
</style>
<table border="1" class="dataframe">
<thead>
<tr style="text-align: right;">
<th></th>
<th>address</th>
<th>sex</th>
</tr>
</thead>
<tbody>
<tr>
<th>0</th>
<td>北京</td>
<td>Male</td>
</tr>
<tr>
<th>1</th>
<td>深圳</td>
<td>Female</td>
</tr>
</tbody>
</table>
</div>
# 使用1:縱向
pd.concat([df1,df2])
<div>
<style scoped>
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</style>
<table border="1" class="dataframe">
<thead>
<tr style="text-align: right;">
<th></th>
<th>sid</th>
<th>name</th>
</tr>
</thead>
<tbody>
<tr>
<th>0</th>
<td>s1</td>
<td>xiaoming</td>
</tr>
<tr>
<th>1</th>
<td>s2</td>
<td>Mike</td>
</tr>
<tr>
<th>0</th>
<td>s3</td>
<td>Tom</td>
</tr>
<tr>
<th>1</th>
<td>s4</td>
<td>Peter</td>
</tr>
</tbody>
</table>
</div>
# 使用2:橫向
pd.concat([df1,df3],axis=1)
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</style>
<table border="1" class="dataframe">
<thead>
<tr style="text-align: right;">
<th></th>
<th>sid</th>
<th>name</th>
<th>address</th>
<th>sex</th>
</tr>
</thead>
<tbody>
<tr>
<th>0</th>
<td>s1</td>
<td>xiaoming</td>
<td>北京</td>
<td>Male</td>
</tr>
<tr>
<th>1</th>
<td>s2</td>
<td>Mike</td>
<td>深圳</td>
<td>Female</td>
</tr>
</tbody>
</table>
</div>
dropna函數(shù)
刪除空值
df4 = pd.DataFrame({
"sid":["s1","s2", np.nan],
"name":["xiaoming",np.nan, "Mike"]})
df4
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<style scoped>
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.dataframe tbody tr th {
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.dataframe thead th {
text-align: right;
}
</style>
<table border="1" class="dataframe">
<thead>
<tr style="text-align: right;">
<th></th>
<th>sid</th>
<th>name</th>
</tr>
</thead>
<tbody>
<tr>
<th>0</th>
<td>s1</td>
<td>xiaoming</td>
</tr>
<tr>
<th>1</th>
<td>s2</td>
<td>NaN</td>
</tr>
<tr>
<th>2</th>
<td>NaN</td>
<td>Mike</td>
</tr>
</tbody>
</table>
</div>
df4.dropna()
<div>
<style scoped>
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.dataframe tbody tr th {
vertical-align: top;
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.dataframe thead th {
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}
</style>
<table border="1" class="dataframe">
<thead>
<tr style="text-align: right;">
<th></th>
<th>sid</th>
<th>name</th>
</tr>
</thead>
<tbody>
<tr>
<th>0</th>
<td>s1</td>
<td>xiaoming</td>
</tr>
</tbody>
</table>
</div>
df4.dropna(subset=["name"])
<div>
<style scoped>
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</style>
<table border="1" class="dataframe">
<thead>
<tr style="text-align: right;">
<th></th>
<th>sid</th>
<th>name</th>
</tr>
</thead>
<tbody>
<tr>
<th>0</th>
<td>s1</td>
<td>xiaoming</td>
</tr>
<tr>
<th>2</th>
<td>NaN</td>
<td>Mike</td>
</tr>
</tbody>
</table>
</div>
explode函數(shù)
爆炸函數(shù)的使用:將寬表轉(zhuǎn)成長表
df5 = pd.DataFrame({
"sid":["s1","s2"],
"phones":[["華為","小米","一加"],["三星","蘋果"]]
})
df5
<div>
<style scoped>
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.dataframe tbody tr th {
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.dataframe thead th {
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</style>
<table border="1" class="dataframe">
<thead>
<tr style="text-align: right;">
<th></th>
<th>sid</th>
<th>phones</th>
</tr>
</thead>
<tbody>
<tr>
<th>0</th>
<td>s1</td>
<td>[華為, 小米, 一加]</td>
</tr>
<tr>
<th>1</th>
<td>s2</td>
<td>[三星, 蘋果]</td>
</tr>
</tbody>
</table>
</div>
df5.explode("phones")
<div>
<style scoped>
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.dataframe tbody tr th {
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</style>
<table border="1" class="dataframe">
<thead>
<tr style="text-align: right;">
<th></th>
<th>sid</th>
<th>phones</th>
</tr>
</thead>
<tbody>
<tr>
<th>0</th>
<td>s1</td>
<td>華為</td>
</tr>
<tr>
<th>0</th>
<td>s1</td>
<td>小米</td>
</tr>
<tr>
<th>0</th>
<td>s1</td>
<td>一加</td>
</tr>
<tr>
<th>1</th>
<td>s2</td>
<td>三星</td>
</tr>
<tr>
<th>1</th>
<td>s2</td>
<td>蘋果</td>
</tr>
</tbody>
</table>
</div>
df5
<div>
<style scoped>
.dataframe tbody tr th:only-of-type {
vertical-align: middle;
}
.dataframe tbody tr th {
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.dataframe thead th {
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</style>
<table border="1" class="dataframe">
<thead>
<tr style="text-align: right;">
<th></th>
<th>sid</th>
<th>phones</th>
</tr>
</thead>
<tbody>
<tr>
<th>0</th>
<td>s1</td>
<td>[華為, 小米, 一加]</td>
</tr>
<tr>
<th>1</th>
<td>s2</td>
<td>[三星, 蘋果]</td>
</tr>
</tbody>
</table>
</div>
fillna函數(shù)
填充缺失值
df4
<div>
<style scoped>
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vertical-align: middle;
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.dataframe tbody tr th {
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.dataframe thead th {
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}
</style>
<table border="1" class="dataframe">
<thead>
<tr style="text-align: right;">
<th></th>
<th>sid</th>
<th>name</th>
</tr>
</thead>
<tbody>
<tr>
<th>0</th>
<td>s1</td>
<td>xiaoming</td>
</tr>
<tr>
<th>1</th>
<td>s2</td>
<td>NaN</td>
</tr>
<tr>
<th>2</th>
<td>NaN</td>
<td>Mike</td>
</tr>
</tbody>
</table>
</div>
df4.fillna({"sid":"s3","name":"Peter"})
<div>
<style scoped>
.dataframe tbody tr th:only-of-type {
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.dataframe tbody tr th {
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.dataframe thead th {
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}
</style>
<table border="1" class="dataframe">
<thead>
<tr style="text-align: right;">
<th></th>
<th>sid</th>
<th>name</th>
</tr>
</thead>
<tbody>
<tr>
<th>0</th>
<td>s1</td>
<td>xiaoming</td>
</tr>
<tr>
<th>1</th>
<td>s2</td>
<td>Peter</td>
</tr>
<tr>
<th>2</th>
<td>s3</td>
<td>Mike</td>
</tr>
</tbody>
</table>
</div>
groupby函數(shù)
同組統(tǒng)計的功能
# 借用這個結(jié)果
df6 = df5.explode("phones")
df6
<div>
<style scoped>
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.dataframe tbody tr th {
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.dataframe thead th {
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</style>
<table border="1" class="dataframe">
<thead>
<tr style="text-align: right;">
<th></th>
<th>sid</th>
<th>phones</th>
</tr>
</thead>
<tbody>
<tr>
<th>0</th>
<td>s1</td>
<td>華為</td>
</tr>
<tr>
<th>0</th>
<td>s1</td>
<td>小米</td>
</tr>
<tr>
<th>0</th>
<td>s1</td>
<td>一加</td>
</tr>
<tr>
<th>1</th>
<td>s2</td>
<td>三星</td>
</tr>
<tr>
<th>1</th>
<td>s2</td>
<td>蘋果</td>
</tr>
</tbody>
</table>
</div>
df6.groupby("sid")["phones"].count()
sid
s1 3
s2 2
Name: phones, dtype: int64
head函數(shù)
查看前幾行的數(shù)據(jù)拼缝,默認是前5行
df7 = pd.DataFrame({
"sid":list(range(10)),
"name":list(range(80,100,2))})
df7
<div>
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</style>
<table border="1" class="dataframe">
<thead>
<tr style="text-align: right;">
<th></th>
<th>sid</th>
<th>name</th>
</tr>
</thead>
<tbody>
<tr>
<th>0</th>
<td>0</td>
<td>80</td>
</tr>
<tr>
<th>1</th>
<td>1</td>
<td>82</td>
</tr>
<tr>
<th>2</th>
<td>2</td>
<td>84</td>
</tr>
<tr>
<th>3</th>
<td>3</td>
<td>86</td>
</tr>
<tr>
<th>4</th>
<td>4</td>
<td>88</td>
</tr>
<tr>
<th>5</th>
<td>5</td>
<td>90</td>
</tr>
<tr>
<th>6</th>
<td>6</td>
<td>92</td>
</tr>
<tr>
<th>7</th>
<td>7</td>
<td>94</td>
</tr>
<tr>
<th>8</th>
<td>8</td>
<td>96</td>
</tr>
<tr>
<th>9</th>
<td>9</td>
<td>98</td>
</tr>
</tbody>
</table>
</div>
df7.head() # 默認前5行
<div>
<style scoped>
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</style>
<table border="1" class="dataframe">
<thead>
<tr style="text-align: right;">
<th></th>
<th>sid</th>
<th>name</th>
</tr>
</thead>
<tbody>
<tr>
<th>0</th>
<td>0</td>
<td>80</td>
</tr>
<tr>
<th>1</th>
<td>1</td>
<td>82</td>
</tr>
<tr>
<th>2</th>
<td>2</td>
<td>84</td>
</tr>
<tr>
<th>3</th>
<td>3</td>
<td>86</td>
</tr>
<tr>
<th>4</th>
<td>4</td>
<td>88</td>
</tr>
</tbody>
</table>
</div>
df7.head(3) # 指定前3行
<div>
<style scoped>
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.dataframe tbody tr th {
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.dataframe thead th {
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</style>
<table border="1" class="dataframe">
<thead>
<tr style="text-align: right;">
<th></th>
<th>sid</th>
<th>name</th>
</tr>
</thead>
<tbody>
<tr>
<th>0</th>
<td>0</td>
<td>80</td>
</tr>
<tr>
<th>1</th>
<td>1</td>
<td>82</td>
</tr>
<tr>
<th>2</th>
<td>2</td>
<td>84</td>
</tr>
</tbody>
</table>
</div>
isnull函數(shù)
判斷是否存在缺失值娱局,超級常用的函數(shù)
df4
<div>
<style scoped>
.dataframe tbody tr th:only-of-type {
vertical-align: middle;
}
.dataframe tbody tr th {
vertical-align: top;
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.dataframe thead th {
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}
</style>
<table border="1" class="dataframe">
<thead>
<tr style="text-align: right;">
<th></th>
<th>sid</th>
<th>name</th>
</tr>
</thead>
<tbody>
<tr>
<th>0</th>
<td>s1</td>
<td>xiaoming</td>
</tr>
<tr>
<th>1</th>
<td>s2</td>
<td>NaN</td>
</tr>
<tr>
<th>2</th>
<td>NaN</td>
<td>Mike</td>
</tr>
</tbody>
</table>
</div>
df4.isnull() # True表示缺失
<div>
<style scoped>
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.dataframe tbody tr th {
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.dataframe thead th {
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</style>
<table border="1" class="dataframe">
<thead>
<tr style="text-align: right;">
<th></th>
<th>sid</th>
<th>name</th>
</tr>
</thead>
<tbody>
<tr>
<th>0</th>
<td>False</td>
<td>False</td>
</tr>
<tr>
<th>1</th>
<td>False</td>
<td>True</td>
</tr>
<tr>
<th>2</th>
<td>True</td>
<td>False</td>
</tr>
</tbody>
</table>
</div>
df4.isnull().sum() # 每個字段缺失的總和
sid 1
name 1
dtype: int64
df6.isnull().sum() # 沒有缺失值
sid 0
phones 0
dtype: int64
join函數(shù)
用于連接不同的DataFrame:
df7 = pd.DataFrame({
'key': ['K0', 'K1', 'K2', 'K3', 'K4', 'K5'],
'A': ['A0', 'A1', 'A2', 'A3', 'A4', 'A5']})
df7
<div>
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</style>
<table border="1" class="dataframe">
<thead>
<tr style="text-align: right;">
<th></th>
<th>key</th>
<th>A</th>
</tr>
</thead>
<tbody>
<tr>
<th>0</th>
<td>K0</td>
<td>A0</td>
</tr>
<tr>
<th>1</th>
<td>K1</td>
<td>A1</td>
</tr>
<tr>
<th>2</th>
<td>K2</td>
<td>A2</td>
</tr>
<tr>
<th>3</th>
<td>K3</td>
<td>A3</td>
</tr>
<tr>
<th>4</th>
<td>K4</td>
<td>A4</td>
</tr>
<tr>
<th>5</th>
<td>K5</td>
<td>A5</td>
</tr>
</tbody>
</table>
</div>
df8 = pd.DataFrame({
'key': ['K0', 'K1', 'K2'],
'B': ['B0', 'B1', 'B2']})
df8
<div>
<style scoped>
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</style>
<table border="1" class="dataframe">
<thead>
<tr style="text-align: right;">
<th></th>
<th>key</th>
<th>B</th>
</tr>
</thead>
<tbody>
<tr>
<th>0</th>
<td>K0</td>
<td>B0</td>
</tr>
<tr>
<th>1</th>
<td>K1</td>
<td>B1</td>
</tr>
<tr>
<th>2</th>
<td>K2</td>
<td>B2</td>
</tr>
</tbody>
</table>
</div>
df7.join(df8,lsuffix="_df7",rsuffix="_df8")
<div>
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<table border="1" class="dataframe">
<thead>
<tr style="text-align: right;">
<th></th>
<th>key_df7</th>
<th>A</th>
<th>key_df8</th>
<th>B</th>
</tr>
</thead>
<tbody>
<tr>
<th>0</th>
<td>K0</td>
<td>A0</td>
<td>K0</td>
<td>B0</td>
</tr>
<tr>
<th>1</th>
<td>K1</td>
<td>A1</td>
<td>K1</td>
<td>B1</td>
</tr>
<tr>
<th>2</th>
<td>K2</td>
<td>A2</td>
<td>K2</td>
<td>B2</td>
</tr>
<tr>
<th>3</th>
<td>K3</td>
<td>A3</td>
<td>NaN</td>
<td>NaN</td>
</tr>
<tr>
<th>4</th>
<td>K4</td>
<td>A4</td>
<td>NaN</td>
<td>NaN</td>
</tr>
<tr>
<th>5</th>
<td>K5</td>
<td>A5</td>
<td>NaN</td>
<td>NaN</td>
</tr>
</tbody>
</table>
</div>
kurt函數(shù)
查找數(shù)據(jù)的峰度值
df9 = pd.DataFrame({
"A":[12, 4, 5, 44, 1],
"B":[5, 2, 54, 3, 2],
"C":[20, 16, 7, 3, 8],
"D":[14, 3, 17, 2, 6]})
df9
<div>
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</style>
<table border="1" class="dataframe">
<thead>
<tr style="text-align: right;">
<th></th>
<th>A</th>
<th>B</th>
<th>C</th>
<th>D</th>
</tr>
</thead>
<tbody>
<tr>
<th>0</th>
<td>12</td>
<td>5</td>
<td>20</td>
<td>14</td>
</tr>
<tr>
<th>1</th>
<td>4</td>
<td>2</td>
<td>16</td>
<td>3</td>
</tr>
<tr>
<th>2</th>
<td>5</td>
<td>54</td>
<td>7</td>
<td>17</td>
</tr>
<tr>
<th>3</th>
<td>44</td>
<td>3</td>
<td>3</td>
<td>2</td>
</tr>
<tr>
<th>4</th>
<td>1</td>
<td>2</td>
<td>8</td>
<td>6</td>
</tr>
</tbody>
</table>
</div>
df9.kurt()
A 3.936824
B 4.941512
C -1.745717
D -2.508808
dtype: float64
loc函數(shù)
loc就是location的縮寫,定位查找數(shù)據(jù)
df9
<div>
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</style>
<table border="1" class="dataframe">
<thead>
<tr style="text-align: right;">
<th></th>
<th>A</th>
<th>B</th>
<th>C</th>
<th>D</th>
</tr>
</thead>
<tbody>
<tr>
<th>0</th>
<td>12</td>
<td>5</td>
<td>20</td>
<td>14</td>
</tr>
<tr>
<th>1</th>
<td>4</td>
<td>2</td>
<td>16</td>
<td>3</td>
</tr>
<tr>
<th>2</th>
<td>5</td>
<td>54</td>
<td>7</td>
<td>17</td>
</tr>
<tr>
<th>3</th>
<td>44</td>
<td>3</td>
<td>3</td>
<td>2</td>
</tr>
<tr>
<th>4</th>
<td>1</td>
<td>2</td>
<td>8</td>
<td>6</td>
</tr>
</tbody>
</table>
</div>
df9.loc[1,:] # 第一行全部列的數(shù)據(jù)
A 4
B 2
C 16
D 3
Name: 1, dtype: int64
df9.loc[1:3,"B"] # 1到3行的B列
1 2
2 54
3 3
Name: B, dtype: int64
merge函數(shù)
同樣也是數(shù)據(jù)的合并函數(shù)咧七,類似SQL中的join衰齐,功能最為強大
df7
<div>
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</style>
<table border="1" class="dataframe">
<thead>
<tr style="text-align: right;">
<th></th>
<th>key</th>
<th>A</th>
</tr>
</thead>
<tbody>
<tr>
<th>0</th>
<td>K0</td>
<td>A0</td>
</tr>
<tr>
<th>1</th>
<td>K1</td>
<td>A1</td>
</tr>
<tr>
<th>2</th>
<td>K2</td>
<td>A2</td>
</tr>
<tr>
<th>3</th>
<td>K3</td>
<td>A3</td>
</tr>
<tr>
<th>4</th>
<td>K4</td>
<td>A4</td>
</tr>
<tr>
<th>5</th>
<td>K5</td>
<td>A5</td>
</tr>
</tbody>
</table>
</div>
df8
<div>
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</style>
<table border="1" class="dataframe">
<thead>
<tr style="text-align: right;">
<th></th>
<th>key</th>
<th>B</th>
</tr>
</thead>
<tbody>
<tr>
<th>0</th>
<td>K0</td>
<td>B0</td>
</tr>
<tr>
<th>1</th>
<td>K1</td>
<td>B1</td>
</tr>
<tr>
<th>2</th>
<td>K2</td>
<td>B2</td>
</tr>
</tbody>
</table>
</div>
pd.merge(df7,df8) # 默認how的參數(shù)是inner
<div>
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<table border="1" class="dataframe">
<thead>
<tr style="text-align: right;">
<th></th>
<th>key</th>
<th>A</th>
<th>B</th>
</tr>
</thead>
<tbody>
<tr>
<th>0</th>
<td>K0</td>
<td>A0</td>
<td>B0</td>
</tr>
<tr>
<th>1</th>
<td>K1</td>
<td>A1</td>
<td>B1</td>
</tr>
<tr>
<th>2</th>
<td>K2</td>
<td>A2</td>
<td>B2</td>
</tr>
</tbody>
</table>
</div>
pd.merge(df7,df8,how="outer")
<div>
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</style>
<table border="1" class="dataframe">
<thead>
<tr style="text-align: right;">
<th></th>
<th>key</th>
<th>A</th>
<th>B</th>
</tr>
</thead>
<tbody>
<tr>
<th>0</th>
<td>K0</td>
<td>A0</td>
<td>B0</td>
</tr>
<tr>
<th>1</th>
<td>K1</td>
<td>A1</td>
<td>B1</td>
</tr>
<tr>
<th>2</th>
<td>K2</td>
<td>A2</td>
<td>B2</td>
</tr>
<tr>
<th>3</th>
<td>K3</td>
<td>A3</td>
<td>NaN</td>
</tr>
<tr>
<th>4</th>
<td>K4</td>
<td>A4</td>
<td>NaN</td>
</tr>
<tr>
<th>5</th>
<td>K5</td>
<td>A5</td>
<td>NaN</td>
</tr>
</tbody>
</table>
</div>
nunique函數(shù)
用于統(tǒng)計數(shù)據(jù)的唯一值
df10 = pd.DataFrame({
"sid":list("acbdefg"),
"score":[9,8,9,7,8,9,3]
})
df10
<div>
<style scoped>
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</style>
<table border="1" class="dataframe">
<thead>
<tr style="text-align: right;">
<th></th>
<th>sid</th>
<th>score</th>
</tr>
</thead>
<tbody>
<tr>
<th>0</th>
<td>a</td>
<td>9</td>
</tr>
<tr>
<th>1</th>
<td>c</td>
<td>8</td>
</tr>
<tr>
<th>2</th>
<td>b</td>
<td>9</td>
</tr>
<tr>
<th>3</th>
<td>d</td>
<td>7</td>
</tr>
<tr>
<th>4</th>
<td>e</td>
<td>8</td>
</tr>
<tr>
<th>5</th>
<td>f</td>
<td>9</td>
</tr>
<tr>
<th>6</th>
<td>g</td>
<td>3</td>
</tr>
</tbody>
</table>
</div>
df10.nunique()
sid 7
score 4
dtype: int64
pct_change函數(shù)
計算當前時期和前一個時期的比值
s = pd.Series([90, 91, 85])
s
0 90
1 91
2 85
dtype: int64
s.pct_change()
0 NaN
1 0.011111
2 -0.065934
dtype: float64
(91 - 90) / 90
0.011111111111111112
(85 - 91) / 91
-0.06593406593406594
# 和前兩個時期相比
s.pct_change(periods=2)
0 NaN
1 NaN
2 -0.055556
dtype: float64
# 如果存在空值,用填充方法
s = pd.Series([90, 91, None, 85])
s
0 90.0
1 91.0
2 NaN
3 85.0
dtype: float64
s.pct_change(fill_method='ffill')
0 NaN
1 0.011111
2 0.000000
3 -0.065934
dtype: float64
query函數(shù)
根據(jù)條件查詢?nèi)≈?/p>
df10
<div>
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</style>
<table border="1" class="dataframe">
<thead>
<tr style="text-align: right;">
<th></th>
<th>sid</th>
<th>score</th>
</tr>
</thead>
<tbody>
<tr>
<th>0</th>
<td>a</td>
<td>9</td>
</tr>
<tr>
<th>1</th>
<td>c</td>
<td>8</td>
</tr>
<tr>
<th>2</th>
<td>b</td>
<td>9</td>
</tr>
<tr>
<th>3</th>
<td>d</td>
<td>7</td>
</tr>
<tr>
<th>4</th>
<td>e</td>
<td>8</td>
</tr>
<tr>
<th>5</th>
<td>f</td>
<td>9</td>
</tr>
<tr>
<th>6</th>
<td>g</td>
<td>3</td>
</tr>
</tbody>
</table>
</div>
df10.query("score >= 8")
<div>
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<table border="1" class="dataframe">
<thead>
<tr style="text-align: right;">
<th></th>
<th>sid</th>
<th>score</th>
</tr>
</thead>
<tbody>
<tr>
<th>0</th>
<td>a</td>
<td>9</td>
</tr>
<tr>
<th>1</th>
<td>c</td>
<td>8</td>
</tr>
<tr>
<th>2</th>
<td>b</td>
<td>9</td>
</tr>
<tr>
<th>4</th>
<td>e</td>
<td>8</td>
</tr>
<tr>
<th>5</th>
<td>f</td>
<td>9</td>
</tr>
</tbody>
</table>
</div>
rank函數(shù)
進行排名的函數(shù)继阻,類似SQL的窗口函數(shù)功能:
df10
<div>
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</style>
<table border="1" class="dataframe">
<thead>
<tr style="text-align: right;">
<th></th>
<th>sid</th>
<th>score</th>
</tr>
</thead>
<tbody>
<tr>
<th>0</th>
<td>a</td>
<td>9</td>
</tr>
<tr>
<th>1</th>
<td>c</td>
<td>8</td>
</tr>
<tr>
<th>2</th>
<td>b</td>
<td>9</td>
</tr>
<tr>
<th>3</th>
<td>d</td>
<td>7</td>
</tr>
<tr>
<th>4</th>
<td>e</td>
<td>8</td>
</tr>
<tr>
<th>5</th>
<td>f</td>
<td>9</td>
</tr>
<tr>
<th>6</th>
<td>g</td>
<td>3</td>
</tr>
</tbody>
</table>
</div>
df10["rank_10"] = df10["score"].rank()
df10
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<table border="1" class="dataframe">
<thead>
<tr style="text-align: right;">
<th></th>
<th>sid</th>
<th>score</th>
<th>rank_10</th>
</tr>
</thead>
<tbody>
<tr>
<th>0</th>
<td>a</td>
<td>9</td>
<td>6.0</td>
</tr>
<tr>
<th>1</th>
<td>c</td>
<td>8</td>
<td>3.5</td>
</tr>
<tr>
<th>2</th>
<td>b</td>
<td>9</td>
<td>6.0</td>
</tr>
<tr>
<th>3</th>
<td>d</td>
<td>7</td>
<td>2.0</td>
</tr>
<tr>
<th>4</th>
<td>e</td>
<td>8</td>
<td>3.5</td>
</tr>
<tr>
<th>5</th>
<td>f</td>
<td>9</td>
<td>6.0</td>
</tr>
<tr>
<th>6</th>
<td>g</td>
<td>3</td>
<td>1.0</td>
</tr>
</tbody>
</table>
</div>
df10["rank_10_max"] = df10["score"].rank(method="max")
df10
<div>
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</style>
<table border="1" class="dataframe">
<thead>
<tr style="text-align: right;">
<th></th>
<th>sid</th>
<th>score</th>
<th>rank_10</th>
<th>rank_10_max</th>
</tr>
</thead>
<tbody>
<tr>
<th>0</th>
<td>a</td>
<td>9</td>
<td>6.0</td>
<td>7.0</td>
</tr>
<tr>
<th>1</th>
<td>c</td>
<td>8</td>
<td>3.5</td>
<td>4.0</td>
</tr>
<tr>
<th>2</th>
<td>b</td>
<td>9</td>
<td>6.0</td>
<td>7.0</td>
</tr>
<tr>
<th>3</th>
<td>d</td>
<td>7</td>
<td>2.0</td>
<td>2.0</td>
</tr>
<tr>
<th>4</th>
<td>e</td>
<td>8</td>
<td>3.5</td>
<td>4.0</td>
</tr>
<tr>
<th>5</th>
<td>f</td>
<td>9</td>
<td>6.0</td>
<td>7.0</td>
</tr>
<tr>
<th>6</th>
<td>g</td>
<td>3</td>
<td>1.0</td>
<td>1.0</td>
</tr>
</tbody>
</table>
</div>
df10["rank_10_min"] = df10["score"].rank(method="min")
df10
<div>
<style scoped>
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</style>
<table border="1" class="dataframe">
<thead>
<tr style="text-align: right;">
<th></th>
<th>sid</th>
<th>score</th>
<th>rank_10</th>
<th>rank_10_max</th>
<th>rank_10_min</th>
</tr>
</thead>
<tbody>
<tr>
<th>0</th>
<td>a</td>
<td>9</td>
<td>6.0</td>
<td>7.0</td>
<td>5.0</td>
</tr>
<tr>
<th>1</th>
<td>c</td>
<td>8</td>
<td>3.5</td>
<td>4.0</td>
<td>3.0</td>
</tr>
<tr>
<th>2</th>
<td>b</td>
<td>9</td>
<td>6.0</td>
<td>7.0</td>
<td>5.0</td>
</tr>
<tr>
<th>3</th>
<td>d</td>
<td>7</td>
<td>2.0</td>
<td>2.0</td>
<td>2.0</td>
</tr>
<tr>
<th>4</th>
<td>e</td>
<td>8</td>
<td>3.5</td>
<td>4.0</td>
<td>3.0</td>
</tr>
<tr>
<th>5</th>
<td>f</td>
<td>9</td>
<td>6.0</td>
<td>7.0</td>
<td>5.0</td>
</tr>
<tr>
<th>6</th>
<td>g</td>
<td>3</td>
<td>1.0</td>
<td>1.0</td>
<td>1.0</td>
</tr>
</tbody>
</table>
</div>
sort_values函數(shù)
根據(jù)數(shù)據(jù)進行排序的函數(shù)
df9
<div>
<style scoped>
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</style>
<table border="1" class="dataframe">
<thead>
<tr style="text-align: right;">
<th></th>
<th>A</th>
<th>B</th>
<th>C</th>
<th>D</th>
</tr>
</thead>
<tbody>
<tr>
<th>0</th>
<td>12</td>
<td>5</td>
<td>20</td>
<td>14</td>
</tr>
<tr>
<th>1</th>
<td>4</td>
<td>2</td>
<td>16</td>
<td>3</td>
</tr>
<tr>
<th>2</th>
<td>5</td>
<td>54</td>
<td>7</td>
<td>17</td>
</tr>
<tr>
<th>3</th>
<td>44</td>
<td>3</td>
<td>3</td>
<td>2</td>
</tr>
<tr>
<th>4</th>
<td>1</td>
<td>2</td>
<td>8</td>
<td>6</td>
</tr>
</tbody>
</table>
</div>
df9.sort_values("A") # 默認是升序排列
<div>
<style scoped>
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}
</style>
<table border="1" class="dataframe">
<thead>
<tr style="text-align: right;">
<th></th>
<th>A</th>
<th>B</th>
<th>C</th>
<th>D</th>
</tr>
</thead>
<tbody>
<tr>
<th>4</th>
<td>1</td>
<td>2</td>
<td>8</td>
<td>6</td>
</tr>
<tr>
<th>1</th>
<td>4</td>
<td>2</td>
<td>16</td>
<td>3</td>
</tr>
<tr>
<th>2</th>
<td>5</td>
<td>54</td>
<td>7</td>
<td>17</td>
</tr>
<tr>
<th>0</th>
<td>12</td>
<td>5</td>
<td>20</td>
<td>14</td>
</tr>
<tr>
<th>3</th>
<td>44</td>
<td>3</td>
<td>3</td>
<td>2</td>
</tr>
</tbody>
</table>
</div>
# 先根據(jù)B升序耻涛,如果B相同废酷,再根據(jù)D降序
df9.sort_values(["B","D"], ascending=[True,False])
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<table border="1" class="dataframe">
<thead>
<tr style="text-align: right;">
<th></th>
<th>A</th>
<th>B</th>
<th>C</th>
<th>D</th>
</tr>
</thead>
<tbody>
<tr>
<th>4</th>
<td>1</td>
<td>2</td>
<td>8</td>
<td>6</td>
</tr>
<tr>
<th>1</th>
<td>4</td>
<td>2</td>
<td>16</td>
<td>3</td>
</tr>
<tr>
<th>3</th>
<td>44</td>
<td>3</td>
<td>3</td>
<td>2</td>
</tr>
<tr>
<th>0</th>
<td>12</td>
<td>5</td>
<td>20</td>
<td>14</td>
</tr>
<tr>
<th>2</th>
<td>5</td>
<td>54</td>
<td>7</td>
<td>17</td>
</tr>
</tbody>
</table>
</div>
tail函數(shù)
查看末尾的數(shù)據(jù)
df7.tail()
<div>
<style scoped>
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</style>
<table border="1" class="dataframe">
<thead>
<tr style="text-align: right;">
<th></th>
<th>key</th>
<th>A</th>
</tr>
</thead>
<tbody>
<tr>
<th>1</th>
<td>K1</td>
<td>A1</td>
</tr>
<tr>
<th>2</th>
<td>K2</td>
<td>A2</td>
</tr>
<tr>
<th>3</th>
<td>K3</td>
<td>A3</td>
</tr>
<tr>
<th>4</th>
<td>K4</td>
<td>A4</td>
</tr>
<tr>
<th>5</th>
<td>K5</td>
<td>A5</td>
</tr>
</tbody>
</table>
</div>
df7.tail(3)
<div>
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</style>
<table border="1" class="dataframe">
<thead>
<tr style="text-align: right;">
<th></th>
<th>key</th>
<th>A</th>
</tr>
</thead>
<tbody>
<tr>
<th>3</th>
<td>K3</td>
<td>A3</td>
</tr>
<tr>
<th>4</th>
<td>K4</td>
<td>A4</td>
</tr>
<tr>
<th>5</th>
<td>K5</td>
<td>A5</td>
</tr>
</tbody>
</table>
</div>
unique函數(shù)
查找每個字段的唯一元素
df10
<div>
<style scoped>
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</style>
<table border="1" class="dataframe">
<thead>
<tr style="text-align: right;">
<th></th>
<th>sid</th>
<th>score</th>
<th>rank_10</th>
<th>rank_10_max</th>
<th>rank_10_min</th>
</tr>
</thead>
<tbody>
<tr>
<th>0</th>
<td>a</td>
<td>9</td>
<td>6.0</td>
<td>7.0</td>
<td>5.0</td>
</tr>
<tr>
<th>1</th>
<td>c</td>
<td>8</td>
<td>3.5</td>
<td>4.0</td>
<td>3.0</td>
</tr>
<tr>
<th>2</th>
<td>b</td>
<td>9</td>
<td>6.0</td>
<td>7.0</td>
<td>5.0</td>
</tr>
<tr>
<th>3</th>
<td>d</td>
<td>7</td>
<td>2.0</td>
<td>2.0</td>
<td>2.0</td>
</tr>
<tr>
<th>4</th>
<td>e</td>
<td>8</td>
<td>3.5</td>
<td>4.0</td>
<td>3.0</td>
</tr>
<tr>
<th>5</th>
<td>f</td>
<td>9</td>
<td>6.0</td>
<td>7.0</td>
<td>5.0</td>
</tr>
<tr>
<th>6</th>
<td>g</td>
<td>3</td>
<td>1.0</td>
<td>1.0</td>
<td>1.0</td>
</tr>
</tbody>
</table>
</div>
df10["score"].unique()
array([9, 8, 7, 3])
df10["rank_10"].unique()
array([6. , 3.5, 2. , 1. ])
value_counts函數(shù)
用于統(tǒng)計字段中每個唯一值的個數(shù)
df6
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<table border="1" class="dataframe">
<thead>
<tr style="text-align: right;">
<th></th>
<th>sid</th>
<th>phones</th>
</tr>
</thead>
<tbody>
<tr>
<th>0</th>
<td>s1</td>
<td>華為</td>
</tr>
<tr>
<th>0</th>
<td>s1</td>
<td>小米</td>
</tr>
<tr>
<th>0</th>
<td>s1</td>
<td>一加</td>
</tr>
<tr>
<th>1</th>
<td>s2</td>
<td>三星</td>
</tr>
<tr>
<th>1</th>
<td>s2</td>
<td>蘋果</td>
</tr>
</tbody>
</table>
</div>
df6["sid"].value_counts()
s1 3
s2 2
Name: sid, dtype: int64
df6["phones"].value_counts()
華為 1
蘋果 1
三星 1
一加 1
小米 1
Name: phones, dtype: int64
where函數(shù)
用于查找Series或者DataFrame中滿足某個條件的數(shù)據(jù)
w = pd.Series(range(7))
w
0 0
1 1
2 2
3 3
4 4
5 5
6 6
dtype: int64
# 滿足條件的顯示;不滿足的用空值代替
w.where(w>3)
0 NaN
1 NaN
2 NaN
3 NaN
4 4.0
5 5.0
6 6.0
dtype: float64
# 不滿足條件的用8代替
w.where(w > 1, 8)
0 8
1 8
2 2
3 3
4 4
5 5
6 6
dtype: int64
xs函數(shù)
該函數(shù)是用于多層級索引中用于獲取指定索引處的值抹缕,使用一個關(guān)鍵參數(shù)來選擇多索引特定級別的數(shù)據(jù)澈蟆。
d = {'num_legs': [4, 4, 2, 2],
'num_wings': [0, 0, 2, 2],
'class': ['mammal', 'mammal', 'mammal', 'bird'],
'animal': ['cat', 'dog', 'bat', 'penguin'],
'locomotion': ['walks', 'walks', 'flies', 'walks']}
# 生成數(shù)據(jù)
df11 = pd.DataFrame(data=d)
# 重置索引
df11 = df11.set_index(['class', 'animal', 'locomotion'])
df11
<div>
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<table border="1" class="dataframe">
<thead>
<tr style="text-align: right;">
<th></th>
<th></th>
<th></th>
<th>num_legs</th>
<th>num_wings</th>
</tr>
<tr>
<th>class</th>
<th>animal</th>
<th>locomotion</th>
<th></th>
<th></th>
</tr>
</thead>
<tbody>
<tr>
<th rowspan="3" valign="top">mammal</th>
<th>cat</th>
<th>walks</th>
<td>4</td>
<td>0</td>
</tr>
<tr>
<th>dog</th>
<th>walks</th>
<td>4</td>
<td>0</td>
</tr>
<tr>
<th>bat</th>
<th>flies</th>
<td>2</td>
<td>2</td>
</tr>
<tr>
<th>bird</th>
<th>penguin</th>
<th>walks</th>
<td>2</td>
<td>2</td>
</tr>
</tbody>
</table>
</div>
# 獲取指定索引的值
df11.xs('mammal')
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<table border="1" class="dataframe">
<thead>
<tr style="text-align: right;">
<th></th>
<th></th>
<th>num_legs</th>
<th>num_wings</th>
</tr>
<tr>
<th>animal</th>
<th>locomotion</th>
<th></th>
<th></th>
</tr>
</thead>
<tbody>
<tr>
<th>cat</th>
<th>walks</th>
<td>4</td>
<td>0</td>
</tr>
<tr>
<th>dog</th>
<th>walks</th>
<td>4</td>
<td>0</td>
</tr>
<tr>
<th>bat</th>
<th>flies</th>
<td>2</td>
<td>2</td>
</tr>
</tbody>
</table>
</div>
# 指定多個索引處的值
df11.xs(('mammal', 'dog'))
/Applications/downloads/anaconda/anaconda3/lib/python3.7/site-packages/IPython/core/interactiveshell.py:2881: PerformanceWarning: indexing past lexsort depth may impact performance.
return runner(coro)
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}
</style>
<table border="1" class="dataframe">
<thead>
<tr style="text-align: right;">
<th></th>
<th>num_legs</th>
<th>num_wings</th>
</tr>
<tr>
<th>locomotion</th>
<th></th>
<th></th>
</tr>
</thead>
<tbody>
<tr>
<th>walks</th>
<td>4</td>
<td>0</td>
</tr>
</tbody>
</table>
</div>
# 獲取指定索引和級別(level)的值
df11.xs('cat', level=1)
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<table border="1" class="dataframe">
<thead>
<tr style="text-align: right;">
<th></th>
<th></th>
<th>num_legs</th>
<th>num_wings</th>
</tr>
<tr>
<th>class</th>
<th>locomotion</th>
<th></th>
<th></th>
</tr>
</thead>
<tbody>
<tr>
<th>mammal</th>
<th>walks</th>
<td>4</td>
<td>0</td>
</tr>
</tbody>
</table>
</div>
df11
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}
</style>
<table border="1" class="dataframe">
<thead>
<tr style="text-align: right;">
<th></th>
<th></th>
<th></th>
<th>num_legs</th>
<th>num_wings</th>
</tr>
<tr>
<th>class</th>
<th>animal</th>
<th>locomotion</th>
<th></th>
<th></th>
</tr>
</thead>
<tbody>
<tr>
<th rowspan="3" valign="top">mammal</th>
<th>cat</th>
<th>walks</th>
<td>4</td>
<td>0</td>
</tr>
<tr>
<th>dog</th>
<th>walks</th>
<td>4</td>
<td>0</td>
</tr>
<tr>
<th>bat</th>
<th>flies</th>
<td>2</td>
<td>2</td>
</tr>
<tr>
<th>bird</th>
<th>penguin</th>
<th>walks</th>
<td>2</td>
<td>2</td>
</tr>
</tbody>
</table>
</div>
# 獲取多個索引和級別的值
df11.xs(('bird', 'walks'),level=[0, 'locomotion'])
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<table border="1" class="dataframe">
<thead>
<tr style="text-align: right;">
<th></th>
<th>num_legs</th>
<th>num_wings</th>
</tr>
<tr>
<th>animal</th>
<th></th>
<th></th>
</tr>
</thead>
<tbody>
<tr>
<th>penguin</th>
<td>2</td>
<td>2</td>
</tr>
</tbody>
</table>
</div>
# 獲取指定列和軸上的值
df11.xs('num_wings', axis=1)
class animal locomotion
mammal cat walks 0
dog walks 0
bat flies 2
bird penguin walks 2
Name: num_wings, dtype: int64