[Pandas][DataFrame]S2_07_Label_Based_elements_selection

bank customers.csv

0.65MB

 

 

In [1]:

import pandas as pd

 

In [3]:

bank_df = pd.read_csv('bank customers.csv', index_col='Surname')
bank_df

 

Out[3]:

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

10000 rows × 13 columns

 

In [4]:

# Sort the dataframe in an alphabetical order
bank_df.sort_index(inplace=True); bank_df

 

Out[4]:

	RowNumber	CustomerId	CreditScore	Geography	Gender	Age	Tenure	Balance	NumOfProducts	HasCrCard	IsActiveMember	EstimatedSalary	Exited
Surname
Abazu	3272	15708791	584	Spain	Male	32	9	85534.83	1	0	0	169137.24	0
Abazu	5110	15576156	710	Spain	Female	28	6	0.00	1	1	0	48426.98	0
Abbie	842	15737792	818	France	Female	31	1	186796.37	1	0	0	178252.63	0
Abbott	2989	15684801	689	France	Male	47	1	93871.95	3	1	0	156878.42	1
Abbott	4574	15693906	645	France	Female	24	3	34547.82	1	1	1	11638.17	0
...	...	...	...	...	...	...	...	...	...	...	...	...	...
Zuev	1031	15681174	730	France	Male	39	1	116537.60	1	0	0	145679.60	0
Zuyev	587	15722010	621	Spain	Male	53	9	170491.84	1	1	0	35588.07	1
Zuyev	6974	15583394	659	Germany	Male	39	8	106259.63	2	1	1	198103.32	0
Zuyeva	6386	15706810	606	Germany	Female	32	1	106301.85	2	0	1	59061.25	0
Zuyeva	7740	15689952	724	Spain	Male	41	5	0.00	1	0	1	115753.94	0

10000 rows × 13 columns

 

In [5]:

# loc is used to filter rows and columns
# loc is label-based meaning (by giving a name of rows, columns)
# rows
bank_df.loc['Zuyev']

 

Out[5]:

	RowNumber	CustomerId	CreditScore	Geography	Gender	Age	Tenure	Balance	NumOfProducts	HasCrCard	IsActiveMember	EstimatedSalary	Exited
Surname
Zuyev	587	15722010	621	Spain	Male	53	9	170491.84	1	1	0	35588.07	1
Zuyev	6974	15583394	659	Germany	Male	39	8	106259.63	2	1	1	198103.32	0

 

In [13]:

# select multiplle rows using "colon :"
# ★ this is inclusive meaning that both("Abbie" and "Abbott") were selected
# this will be different if we use integer based index as iloc[]
bank_df.loc["Abbie":"Abbott"]

 

Out[13]:

	RowNumber	CustomerId	CreditScore	Geography	Gender	Age	Tenure	Balance	NumOfProducts	HasCrCard	IsActiveMember	EstimatedSalary	Exited
Surname
Abbie	842	15737792	818	France	Female	31	1	186796.37	1	0	0	178252.63	0
Abbott	2989	15684801	689	France	Male	47	1	93871.95	3	1	0	156878.42	1
Abbott	4574	15693906	645	France	Female	24	3	34547.82	1	1	1	11638.17	0
Abbott	6839	15680804	850	France	Male	29	6	0.00	2	1	1	10672.54	0
Abbott	2538	15723706	573	France	Female	33	0	90124.64	1	1	0	137476.71	0

 

In [14]:

# Select all elements up to and including 'Abbott' index
bank_df.loc[:"Abbott"]

 

Out[14]:

	RowNumber	CustomerId	CreditScore	Geography	Gender	Age	Tenure	Balance	NumOfProducts	HasCrCard	IsActiveMember	EstimatedSalary	Exited
Surname
Abazu	3272	15708791	584	Spain	Male	32	9	85534.83	1	0	0	169137.24	0
Abazu	5110	15576156	710	Spain	Female	28	6	0.00	1	1	0	48426.98	0
Abbie	842	15737792	818	France	Female	31	1	186796.37	1	0	0	178252.63	0
Abbott	2989	15684801	689	France	Male	47	1	93871.95	3	1	0	156878.42	1
Abbott	4574	15693906	645	France	Female	24	3	34547.82	1	1	1	11638.17	0
Abbott	6839	15680804	850	France	Male	29	6	0.00	2	1	1	10672.54	0
Abbott	2538	15723706	573	France	Female	33	0	90124.64	1	1	0	137476.71	0

 

In [17]:

# select multiple rows
bank_df.loc[['Abbott','Zuyev']]

 

Out[17]:

	RowNumber	CustomerId	CreditScore	Geography	Gender	Age	Tenure	Balance	NumOfProducts	HasCrCard	IsActiveMember	EstimatedSalary	Exited
Surname
Abbott	2989	15684801	689	France	Male	47	1	93871.95	3	1	0	156878.42	1
Abbott	4574	15693906	645	France	Female	24	3	34547.82	1	1	1	11638.17	0
Abbott	6839	15680804	850	France	Male	29	6	0.00	2	1	1	10672.54	0
Abbott	2538	15723706	573	France	Female	33	0	90124.64	1	1	0	137476.71	0
Zuyev	587	15722010	621	Spain	Male	53	9	170491.84	1	1	0	35588.07	1
Zuyev	6974	15583394	659	Germany	Male	39	8	106259.63	2	1	1	198103.32	0

 

In [19]:

# randomly select 
# Setting axis=0 means rows. setting index = 1 means columns
bank_df.sample(n=3,axis=0)

 

Out[19]:

	RowNumber	CustomerId	CreditScore	Geography	Gender	Age	Tenure	Balance	NumOfProducts	HasCrCard	IsActiveMember	EstimatedSalary	Exited
Surname
Chiebuka	3505	15700710	490	France	Female	37	3	116465.53	1	0	1	24435.77	0
Kazantsev	3238	15665760	802	Spain	Male	38	7	0.00	2	0	1	57764.65	0
Romero	4420	15677586	587	Germany	Female	32	3	125445.04	2	1	1	130514.78	0

 

In [21]:

# randomly return apercentage of the rows
bank_df.sample(frac = 0.1, axis=0)

 

Out[21]:

	RowNumber	CustomerId	CreditScore	Geography	Gender	Age	Tenure	Balance	NumOfProducts	HasCrCard	IsActiveMember	EstimatedSalary	Exited
Surname
Bruno	1903	15622017	773	Spain	Female	33	10	0.00	1	1	1	98820.09	0
Warren	9581	15778838	783	France	Male	38	9	114135.17	1	1	0	153269.98	0
Williams	7487	15593694	814	France	Male	49	8	0.00	2	0	0	157822.54	0
Y?an	8171	15641366	599	Germany	Male	61	1	124737.96	1	0	1	90389.61	1
Terry	1836	15583718	696	Germany	Male	38	6	142316.14	1	1	1	8018.49	0
...	...	...	...	...	...	...	...	...	...	...	...	...	...
Marshall	1782	15771636	793	Spain	Female	36	0	0.00	1	0	0	148993.47	0
Mazzi	710	15734886	686	France	Female	34	3	123971.51	2	1	0	147794.63	0
Lynch	4965	15574761	466	France	Female	41	3	33563.95	2	1	0	178994.13	1
McDonald	2505	15625942	619	Spain	Female	45	0	0.00	2	0	0	113645.40	0
Miller	1905	15677382	625	Spain	Female	69	1	107569.96	1	1	1	182336.45	0

1000 rows × 13 columns

 

In [22]:

# columns
bank_df.loc[:,'CreditScore']

 

Out[22]:

Surname
Abazu     584
Abazu     710
Abbie     818
Abbott    689
Abbott    645
         ... 
Zuev      730
Zuyev     621
Zuyev     659
Zuyeva    606
Zuyeva    724
Name: CreditScore, Length: 10000, dtype: int64