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In [18]:

    
import pandas as pd
import matplotlib.pyplot as plt
%pylab inline









    



Populating the interactive namespace from numpy and matplotlib



In [19]:

    
data = pd.read_csv("wages_hours.csv")
data.head()









    Out[19]:






  
    
      
      HRS	RATE	ERSP	ERNO	NEIN	ASSET	AGE	DEP	RACE	SCHOOL
    
  
  
    
      0
      2157\t2.905\t1121\t291\t380\t7250\t38.5\t2.340...
    
    
      1
      2174\t2.970\t1128\t301\t398\t7744\t39.3\t2.335...
    
    
      2
      2062\t2.350\t1214\t326\t185\t3068\t40.1\t2.851...
    
    
      3
      2111\t2.511\t1203\t49\t117\t1632\t22.4\t1.159\...
    
    
      4
      2134\t2.791\t1013\t594\t730\t12710\t57.7\t1.22...



In [20]:

    
data = pd.read_csv("wages_hours.csv", sep = "\t")
data.head()



In [21]:

    
data



In [22]:

    
data2 = data[["AGE", "RATE"]]
data2.head()



In [27]:

    
data_sorted = data2.sort(["AGE"])
data_sorted.head()



In [28]:

    
data_sorted.set_index("AGE", inplace=True)
data_sorted.head()



In [29]:

    
data_sorted.plot()
plt.show()



In [ ]:

	HRS	RATE	ERSP	ERNO	NEIN	ASSET	AGE	DEP	RACE	SCHOOL
0	2157	2.905	1121	291	380	7250	38.5	2.340	32.1	10.5
1	2174	2.970	1128	301	398	7744	39.3	2.335	31.2	10.5
2	2062	2.350	1214	326	185	3068	40.1	2.851	*	8.9
3	2111	2.511	1203	49	117	1632	22.4	1.159	27.5	11.5
4	2134	2.791	1013	594	730	12710	57.7	1.229	32.5	8.8
5	2185	3.040	1135	287	382	7706	38.6	2.602	31.4	10.7
6	2210	3.222	1100	295	474	9338	39.0	2.187	10.1	11.2
7	2105	2.493	1180	310	255	4730	39.9	2.616	71.1	9.3
8	2267	2.838	1298	252	431	8317	38.9	2.024	9.7	11.1
9	2205	2.356	885	264	373	6789	38.8	2.662	25.2	9.5
10	2121	2.922	1251	328	312	5907	39.8	2.287	51.1	10.3
11	2109	2.499	1207	347	271	5069	39.7	3.193	*	8.9
12	2108	2.796	1036	300	259	4614	38.2	2.040	*	9.2
13	2047	2.453	1213	297	139	1987	40.3	2.545	*	9.1
14	2174	3.582	1141	414	498	10239	40.0	2.064	*	11.7
15	2067	2.909	1805	290	239	4439	39.1	2.301	*	10.5
16	2159	2.511	1075	289	308	5621	39.3	2.486	43.6	9.5
17	2257	2.516	1093	176	392	7293	37.9	2.042	*	10.1
18	1985	1.423	553	381	146	1866	40.6	3.833	*	6.6
19	2184	3.636	1091	291	560	11240	39.1	2.328	13.6	11.6
20	2084	2.983	1327	331	296	5653	39.8	2.208	58.4	10.2
21	2051	2.573	1194	279	172	2806	40.0	2.362	77.9	9.1
22	2127	3.262	1226	314	408	8042	39.5	2.259	39.2	10.8
23	2102	3.234	1188	414	352	7557	39.8	2.019	29.8	10.7
24	2098	2.280	973	364	272	4400	40.6	2.661	53.6	8.4
25	2042	2.304	1085	328	140	1739	41.8	2.444	83.1	8.2
26	2181	2.912	1072	304	383	7340	39.0	2.337	30.2	10.2
27	2186	3.015	1122	30	352	7292	37.2	2.046	29.5	10.9
28	2108	2.786	1757	*	506	9658	43.4	*	32.6	10.2
29	2188	3.010	990	366	374	7325	38.4	2.847	30.9	10.6
30	2203	3.273	*	*	430	8221	38.2	2.324	22.1	11.0
31	2077	1.901	350	209	95	1370	37.4	4.158	61.3	8.2
32	2196	3.009	947	294	342	6888	37.5	3.047	31.8	10.6
33	2093	1.899	342	311	120	1425	37.5	4.512	62.8	8.1
34	2173	2.959	1116	296	387	7625	39.2	2.342	31.0	10.5
35	2179	2.971	1128	312	397	7779	39.4	2.341	31.2	10.5
36	2200	2.980	1126	204	393	7885	39.2	2.341	31.0	10.6
37	2052	2.630	*	*	154	3331	40.5	*	45.8	10.3
38	2197	3.413	1078	300	512	10450	39.1	2.297	15.5	11.3

	AGE	RATE
3	22.4	2.511
27	37.2	3.015
31	37.4	1.901
33	37.5	1.899
32	37.5	3.009

	RATE
AGE
22.4	2.511
37.2	3.015
37.4	1.901
37.5	1.899
37.5	3.009

	HRS RATE ERSP ERNO NEIN ASSET AGE DEP RACE SCHOOL
0	2157\t2.905\t1121\t291\t380\t7250\t38.5\t2.340...
1	2174\t2.970\t1128\t301\t398\t7744\t39.3\t2.335...
2	2062\t2.350\t1214\t326\t185\t3068\t40.1\t2.851...
3	2111\t2.511\t1203\t49\t117\t1632\t22.4\t1.159\...
4	2134\t2.791\t1013\t594\t730\t12710\t57.7\t1.22...