import pandas as pd
import numpy as np
## 从字典初始化df
ipl_data = {'Team': ['Riders', 'Riders', 'Devils', 'Devils', 'Kings',
'Kings', 'Kings', 'Kings', 'Riders', 'Royals', 'Royals', 'Riders'],
'Rank': [1, 2, 2, 3, 3,4 ,1 ,1,2 , 4,1,2],
'Year': [2014,2015,2014,2015,2014,2015,2016,2017,2016,2014,2015,2017],
'Points':[876,789,863,673,741,812,756,788,694,701,804,690]}
df = pd.DataFrame(ipl_data)
print(df)
Team Rank Year Points
0 Riders 1 2014 876
1 Riders 2 2015 789
2 Devils 2 2014 863
3 Devils 3 2015 673
4 Kings 3 2014 741
5 Kings 4 2015 812
6 Kings 1 2016 756
7 Kings 1 2017 788
8 Riders 2 2016 694
9 Royals 4 2014 701
10 Royals 1 2015 804
11 Riders 2 2017 690
print(df.groupby('Team')) ## groupby 返回的对象
<pandas.core.groupby.groupby.DataFrameGroupBy object at 0x7fcbff80a240>
print(df.groupby('Team').groups) ##用groups属性来进行查看每个分组
{'Devils': Int64Index([2, 3], dtype='int64'), 'Kings': Int64Index([4, 5, 6, 7], dtype='int64'), 'Riders': Int64Index([0, 1, 8, 11], dtype='int64'), 'Royals': Int64Index([9, 10], dtype='int64')}
## 对groupby 后的结果进行遍历
grouped = df.groupby('Year')
for name,group in grouped:
print(name)
print(group)
2014
Team Rank Year Points
0 Riders 1 2014 876
2 Devils 2 2014 863
4 Kings 3 2014 741
9 Royals 4 2014 701
2015
Team Rank Year Points
1 Riders 2 2015 789
3 Devils 3 2015 673
5 Kings 4 2015 812
10 Royals 1 2015 804
2016
Team Rank Year Points
6 Kings 1 2016 756
8 Riders 2 2016 694
2017
Team Rank Year Points
7 Kings 1 2017 788
11 Riders 2 2017 690
## 从多个groups中获取单个group
grouped = df.groupby('Year')
print(grouped.get_group(2014))
Team Rank Year Points
0 Riders 1 2014 876
2 Devils 2 2014 863
4 Kings 3 2014 741
9 Royals 4 2014 701
## 使用agg聚合函数计算均值
grouped = df.groupby('Year')
print(grouped['Points'].agg('mean'))
Year
2014 795.25
2015 769.50
2016 725.00
2017 739.00
Name: Points, dtype: float64
## 使用agg聚合函数计算数据条数
grouped = df.groupby('Team')
print(grouped.agg(np.size))
Rank Year Points
Team
Devils 2 2 2
Kings 4 4 4
Riders 4 4 4
Royals 2 2 2
## 使用多个agg聚合函数进行计算
grouped = df.groupby('Team')
print(grouped.agg([np.sum, np.mean, np.std]))
print(grouped['Points'].agg([np.sum, np.mean, np.std]))
print(grouped['Points'].agg({'Points':[np.sum, np.mean, np.std],'Rank':[np.mean]})) ## 分别指定不同的聚合函数
Rank Year Points
sum mean std sum mean std sum mean std
Team
Devils 5 2.50 0.707107 4029 2014.5 0.707107 1536 768.00 134.350288
Kings 9 2.25 1.500000 8062 2015.5 1.290994 3097 774.25 31.899582
Riders 7 1.75 0.500000 8062 2015.5 1.290994 3049 762.25 88.567771
Royals 5 2.50 2.121320 4029 2014.5 0.707107 1505 752.50 72.831998
sum mean std
Team
Devils 1536 768.00 134.350288
Kings 3097 774.25 31.899582
Riders 3049 762.25 88.567771
Royals 1505 752.50 72.831998
Points Rank
sum mean std mean
Team
Devils 1536 768.00 134.350288 768.00
Kings 3097 774.25 31.899582 774.25
Riders 3049 762.25 88.567771 762.25
Royals 1505 752.50 72.831998 752.50
/home/disk1/data/tangshengyu_dxm/tools/env_py36/lib/python3.6/site-packages/ipykernel_launcher.py:5: FutureWarning: using a dict on a Series for aggregation
is deprecated and will be removed in a future version
"""
## grouped数据重新生成dataframe
print(df.groupby('Year')['Team'].apply(len).reset_index()) ## 一级列名
print(df.groupby('Year')['Team'].apply(len).to_frame()) ## 多级列名，列变为索引
Year Team
0 2014 4
1 2015 4
2 2016 2
3 2017 2
Team
Year
2014 4
2015 4
2016 2
2017 2
## 更改聚合后的列名
grouped_df = grouped.agg({'Points':['min','max','mean']})
print(grouped_df.columns)
print(grouped_df.columns.values)
grouped_df.columns = ['_'.join(col_tuple) for col_tuple in grouped_df.columns.values]
grouped_df.reset_index()
MultiIndex(levels=[['Points'], ['min', 'max', 'mean']],
labels=[[0, 0, 0], [0, 1, 2]])
[('Points', 'min') ('Points', 'max') ('Points', 'mean')]
|
Team |
Points_min |
Points_max |
Points_mean |
| 0 |
Devils |
673 |
863 |
768.00 |
| 1 |
Kings |
741 |
812 |
774.25 |
| 2 |
Riders |
690 |
876 |
762.25 |
| 3 |
Royals |
701 |
804 |
752.50 |
## group 后的数据进行transform
grouped = df.groupby('Team')
score = lambda x: (x - x.mean())
print(grouped.transform(score))
Rank Year Points
0 -0.75 -1.5 113.75
1 0.25 -0.5 26.75
2 -0.50 -0.5 95.00
3 0.50 0.5 -95.00
4 0.75 -1.5 -33.25
5 1.75 -0.5 37.75
6 -1.25 0.5 -18.25
7 -1.25 1.5 13.75
8 0.25 0.5 -68.25
9 1.50 -0.5 -51.50
10 -1.50 0.5 51.50
11 0.25 1.5 -72.25
## filter 过滤 (返回满足条件的)
grouped = df.groupby('Team')
print(grouped.filter(lambda x: len(x)>3))
Team Rank Year Points
0 Riders 1 2014 876
1 Riders 2 2015 789
4 Kings 3 2014 741
5 Kings 4 2015 812
6 Kings 1 2016 756
7 Kings 1 2017 788
8 Riders 2 2016 694
11 Riders 2 2017 690
## 每个分组的数据量
grouped = df.groupby('Team')
print(grouped.apply(lambda x: len(x)))
print(type(grouped.apply(lambda x: len(x))))
Team
Devils 2
Kings 4
Riders 4
Royals 2
dtype: int64
<class 'pandas.core.series.Series'>
## 多行字符串组合成一行
print(df)
df_grouped = df.groupby(['Year'])['Team'].apply(';'.join).reset_index()
print(df_grouped)
Team Rank Year Points
0 Riders 1 2014 876
1 Riders 2 2015 789
2 Devils 2 2014 863
3 Devils 3 2015 673
4 Kings 3 2014 741
5 Kings 4 2015 812
6 Kings 1 2016 756
7 Kings 1 2017 788
8 Riders 2 2016 694
9 Royals 4 2014 701
10 Royals 1 2015 804
11 Riders 2 2017 690
Year Team
0 2014 Riders;Devils;Kings;Royals
1 2015 Riders;Devils;Kings;Royals
2 2016 Kings;Riders
3 2017 Kings;Riders
## 一行变多行
def explode(df,tar_col_name):
tar_col_list = [tar_col_name]
rem_col_list = df.columns.difference(tar_col_list)
rem_col_list = list(rem_col_list)
df_new = df.set_index(rem_col_list)
df_explode = pd.DataFrame(df_new[tar_col_name].tolist(),index=df_new.index)
df_explode = df_explode.stack().to_frame()
df_explode.columns = tar_col_list
df_explode = df_explode.reset_index(level= rem_col_list)
return df_explode
df_grouped['Team'] = df_grouped['Team'].apply(lambda s:s.split(';')) ## 先split得到list
print(df_grouped)
explode(df_grouped,'Team')
Year Team
0 2014 [Riders, Devils, Kings, Royals]
1 2015 [Riders, Devils, Kings, Royals]
2 2016 [Kings, Riders]
3 2017 [Kings, Riders]
|
Year |
Team |
| 0 |
2014 |
Riders |
| 1 |
2014 |
Devils |
| 2 |
2014 |
Kings |
| 3 |
2014 |
Royals |
| 0 |
2015 |
Riders |
| 1 |
2015 |
Devils |
| 2 |
2015 |
Kings |
| 3 |
2015 |
Royals |
| 0 |
2016 |
Kings |
| 1 |
2016 |
Riders |
| 0 |
2017 |
Kings |
| 1 |
2017 |
Riders |
# 将多列合并成一列
data = [['Alex', 10, 150], ['Bob', 12, 153], ['Clarke', 13, 160], ['Tom', 12, 160]]
df = pd.DataFrame(data, columns=['Name', 'Age', 'Stature'])
print(df)
df_new = df['Age'].astype(str) +'-'+ df['Stature'].astype(str)
print(df_new)
Name Age Stature
0 Alex 10 150
1 Bob 12 153
2 Clarke 13 160
3 Tom 12 160
0 10-150
1 12-153
2 13-160
3 12-160
dtype: object
## 一列拆分成多列
ipl_data = {'Team': ['Riders', 'Riders', 'Devils', 'Devils', 'Kings',
'Kings', 'Kings', 'Kings', 'Riders', 'Royals', 'Royals', 'Riders'],
'Rank': [1, 2, 2, 3, 3,4 ,1 ,1,2 , 4,1,2],
'Year': [2014,2015,2014,2015,2014,2015,2016,2017,2016,2014,2015,2017],
'Points':[876,789,863,673,741,812,756,788,694,701,804,690]}
df = pd.DataFrame(ipl_data)
df_grouped = df.groupby(['Year'])['Team'].apply(';'.join).reset_index()
print(df_grouped)
df_grouped['Team'].str.split(';', expand=True)
Year Team
0 2014 Riders;Devils;Kings;Royals
1 2015 Riders;Devils;Kings;Royals
2 2016 Kings;Riders
3 2017 Kings;Riders
|
0 |
1 |
2 |
3 |
| 0 |
Riders |
Devils |
Kings |
Royals |
| 1 |
Riders |
Devils |
Kings |
Royals |
| 2 |
Kings |
Riders |
None |
None |
| 3 |
Kings |
Riders |
None |
None |
def df2libsvm(df,missing_value='-9999'):
re_list=[]
length=len(df)
for i in range(length):
row_i=df.iloc[i]
row_dict=row_i.to_dict()
row_list=[]
for key in row_dict:
if row_dict[key]==missing_value:
continue
row_list.append('%s:%s'%(key,str(row_dict[key])))
re_list.append(row_list)
return re_list
def libsvm2df():
"""
mydict = [{'b': 2, 'c': 3, 'd': 4},
... {'a': 100, 'c': 300, 'd': 400},
... {'a': 1000, 'b': 2000, 'c': 3000}]
df=pd.DataFrame(mydict)
"""
def calcu_iv(df,feat_col,label_col,good,bad):
import numpy as np
def f(x,label_col,good,bad):
d = {}
d['bin_bad_cnt'] = (x[label_col]==bad).sum()
d['bin_good_cnt'] = (x[label_col]==good).sum()
return pd.Series(d, index=['bin_good_cnt', 'bin_bad_cnt'])
df_woe = df.groupby(feat_col).apply(f,label_col=label_col,good=good,bad=bad).reset_index()
all_good_cnt = df_woe.bin_good_cnt.sum()
all_bad_cnt = df_woe.bin_bad_cnt.sum()
if all_bad_cnt==0:
all_bad_cnt=1
if all_good_cnt==0:
all_good_cnt=1
df_woe = df_woe.replace({'bin_bad_cnt': {0: 0.1}})
df_woe = df_woe.replace({'bin_good_cnt': {0: 0.1}})
df_woe['distribution_good'] = df_woe['bin_good_cnt']/float(all_good_cnt)
df_woe['distribution_bad'] = df_woe['bin_bad_cnt']/float(all_bad_cnt)
df_woe['WoE'] = np.log(df_woe['distribution_good']/df_woe['distribution_bad'])
df_woe['IV'] = df_woe['WoE'] * (df_woe['distribution_good'] - df_woe['distribution_bad'])
df_woe_inf = df_woe[df_woe['WoE']==np.inf]
iv = df_woe['IV'].sum()
return iv,df_woe
pandas groupby agg 应用分位数函数
def percentile(n):
def percentile_(x):
return np.percentile(x, n)
percentile_.__name__ = 'p%s' % n
return percentile_
df_group = df.groupby(['start_site','end_site','mean_mileage','std_mileage','cv'])
df_route = df_group[['route_cnt']].agg('sum')
df_route['diff_mean'] = df_group[['diff']].agg('mean')
df_route['diff_std'] = df_group[['diff']].agg(np.std)
df_route['diff_p10'] = df_group[['diff']].agg(percentile(10))
df_route['diff_p30'] = df_group[['diff']].agg(percentile(30))