[Pandas][DataFrame]S2_10_sorting_and_ordering

bank customers.csv

0.65MB

 

 

In [1]:

import pandas as pd

 

In [2]:

bank_df = pd.read_csv('bank customers.csv')
bank_df

 

Out[2]:

	RowNumber	CustomerId	Surname	CreditScore	Geography	Gender	Age	Tenure	Balance	NumOfProducts	HasCrCard	IsActiveMember	EstimatedSalary	Exited
0	1	15634602	Hargrave	619	France	Female	42	2	0.00	1	1	1	101348.88	1
1	2	15647311	Hill	608	Spain	Female	41	1	83807.86	1	0	1	112542.58	0
2	3	15619304	Onio	502	France	Female	42	8	159660.80	3	1	0	113931.57	1
3	4	15701354	Boni	699	France	Female	39	1	0.00	2	0	0	93826.63	0
4	5	15737888	Mitchell	850	Spain	Female	43	2	125510.82	1	1	1	79084.10	0
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
9995	9996	15606229	Obijiaku	771	France	Male	39	5	0.00	2	1	0	96270.64	0
9996	9997	15569892	Johnstone	516	France	Male	35	10	57369.61	1	1	1	101699.77	0
9997	9998	15584532	Liu	709	France	Female	36	7	0.00	1	0	1	42085.58	1
9998	9999	15682355	Sabbatini	772	Germany	Male	42	3	75075.31	2	1	0	92888.52	1
9999	10000	15628319	Walker	792	France	Female	28	4	130142.79	1	1	0	38190.78	0

10000 rows × 14 columns

 

In [3]:

# sort the values in the dataframe accordng to number of CreditScore
bank_df.sort_values(by='CreditScore', ascending=False, inplace=True)
bank_df

 

Out[3]:

	RowNumber	CustomerId	Surname	CreditScore	Geography	Gender	Age	Tenure	Balance	NumOfProducts	HasCrCard	IsActiveMember	EstimatedSalary	Exited
792	793	15707473	Summers	850	Germany	Female	48	6	111962.99	1	1	0	111755.80	0
4083	4084	15672640	Kambinachi	850	Spain	Female	45	4	114347.85	2	1	1	109089.04	0
4158	4159	15771580	Davison	850	France	Female	38	0	106831.69	1	0	1	148977.72	0
4157	4158	15719793	Watson	850	Spain	Male	62	5	0.00	2	1	1	180243.56	0
7413	7414	15604536	Vachon	850	Germany	Female	31	4	164672.66	1	0	1	61936.10	0
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
8762	8763	15765173	Lin	350	France	Female	60	3	0.00	1	0	0	113796.15	1
1631	1632	15685372	Azubuike	350	Spain	Male	54	1	152677.48	1	1	1	191973.49	1
9624	9625	15668309	Maslow	350	France	Female	40	0	111098.85	1	1	1	172321.21	1
1838	1839	15758813	Campbell	350	Germany	Male	39	0	109733.20	2	0	0	123602.11	1
8723	8724	15803202	Onyekachi	350	France	Male	51	10	0.00	1	1	1	125823.79	1

10000 rows × 14 columns

 

In [4]:

bank_df.sort_index(inplace=True)
bank_df

 

Out[4]:

	RowNumber	CustomerId	Surname	CreditScore	Geography	Gender	Age	Tenure	Balance	NumOfProducts	HasCrCard	IsActiveMember	EstimatedSalary	Exited
0	1	15634602	Hargrave	619	France	Female	42	2	0.00	1	1	1	101348.88	1
1	2	15647311	Hill	608	Spain	Female	41	1	83807.86	1	0	1	112542.58	0
2	3	15619304	Onio	502	France	Female	42	8	159660.80	3	1	0	113931.57	1
3	4	15701354	Boni	699	France	Female	39	1	0.00	2	0	0	93826.63	0
4	5	15737888	Mitchell	850	Spain	Female	43	2	125510.82	1	1	1	79084.10	0
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
9995	9996	15606229	Obijiaku	771	France	Male	39	5	0.00	2	1	0	96270.64	0
9996	9997	15569892	Johnstone	516	France	Male	35	10	57369.61	1	1	1	101699.77	0
9997	9998	15584532	Liu	709	France	Female	36	7	0.00	1	0	1	42085.58	1
9998	9999	15682355	Sabbatini	772	Germany	Male	42	3	75075.31	2	1	0	92888.52	1
9999	10000	15628319	Walker	792	France	Female	28	4	130142.79	1	1	0	38190.78	0

10000 rows × 14 columns

 

In [5]:

# 오름차순이므로 낮은 값일수록 상위 순위
bank_df['Rank'] = bank_df['Balance'].rank(ascending=True, method='min').astype('int')
bank_df

 

Out[5]:

	RowNumber	CustomerId	Surname	CreditScore	Geography	Gender	Age	Tenure	Balance	NumOfProducts	HasCrCard	IsActiveMember	EstimatedSalary	Exited	Rank
0	1	15634602	Hargrave	619	France	Female	42	2	0.00	1	1	1	101348.88	1	1
1	2	15647311	Hill	608	Spain	Female	41	1	83807.86	1	0	1	112542.58	0	4360
2	3	15619304	Onio	502	France	Female	42	8	159660.80	3	1	0	113931.57	1	9412
3	4	15701354	Boni	699	France	Female	39	1	0.00	2	0	0	93826.63	0	1
4	5	15737888	Mitchell	850	Spain	Female	43	2	125510.82	1	1	1	79084.10	0	7314
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
9995	9996	15606229	Obijiaku	771	France	Male	39	5	0.00	2	1	0	96270.64	0	1
9996	9997	15569892	Johnstone	516	France	Male	35	10	57369.61	1	1	1	101699.77	0	3741
9997	9998	15584532	Liu	709	France	Female	36	7	0.00	1	0	1	42085.58	1	1
9998	9999	15682355	Sabbatini	772	Germany	Male	42	3	75075.31	2	1	0	92888.52	1	4044
9999	10000	15628319	Walker	792	France	Female	28	4	130142.79	1	1	0	38190.78	0	7730

10000 rows × 15 columns

 

In [8]:

bank_df['Rank'] = bank_df['Balance'].rank(ascending=True, method='dense').astype('int')
bank_df

 

Out[8]:

	RowNumber	CustomerId	Surname	CreditScore	Geography	Gender	Age	Tenure	Balance	NumOfProducts	HasCrCard	IsActiveMember	EstimatedSalary	Exited	Rank
0	1	15634602	Hargrave	619	France	Female	42	2	0.00	1	1	1	101348.88	1	1
1	2	15647311	Hill	608	Spain	Female	41	1	83807.86	1	0	1	112542.58	0	744
2	3	15619304	Onio	502	France	Female	42	8	159660.80	3	1	0	113931.57	1	5794
3	4	15701354	Boni	699	France	Female	39	1	0.00	2	0	0	93826.63	0	1
4	5	15737888	Mitchell	850	Spain	Female	43	2	125510.82	1	1	1	79084.10	0	3697
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
9995	9996	15606229	Obijiaku	771	France	Male	39	5	0.00	2	1	0	96270.64	0	1
9996	9997	15569892	Johnstone	516	France	Male	35	10	57369.61	1	1	1	101699.77	0	125
9997	9998	15584532	Liu	709	France	Female	36	7	0.00	1	0	1	42085.58	1	1
9998	9999	15682355	Sabbatini	772	Germany	Male	42	3	75075.31	2	1	0	92888.52	1	428
9999	10000	15628319	Walker	792	France	Female	28	4	130142.79	1	1	0	38190.78	0	4113

10000 rows × 15 columns