This article provides a sample solution about Pandas Shell Python Example.
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Solution
Part I. Slicing
1
df = pd.read_csv(“Salaries.csv”)
. . .
In [15]:
5
1
import numpy as np
2
import scipy as sp
3
import pandas as pd
4
import matplotlib.pyplot as plt
5
import seaborn as sns. . .
In [43]:
df.salary
x
1
df.salary
Out[43]:
0 186960
1 93000
2 110515
3 131205
4 104800
…
73 105450
74 104542
75 124312
76 109954
77 109646
Name: salary, Length: 78, dtype: int64
. . .
In [45]:
print (type(df.salary))
xxxxxxxxxx
1
print (type(df.salary))
<class ‘pandas.core.series.Series’>
. . .
In [56]:
var top5 = array.Take(5);
x
1
import itertools
. . .
In [68]:
1
1
df.salary
Out[68]:
0 186960
1 93000
2 110515
3 131205
4 104800
…
73 105450
74 104542
75 124312
76 109954
77 109646
Name: salary, Length: 78, dtype: int64
. . .
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In [71]:
df.salary[0:10]
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1
df.salary[0:10]
Out[71]:
0 186960
1 93000
2 110515
3 131205
4 104800
5 122400
6 81285
7 126300
8 94350
9 57800
Name: salary, dtype: int64
. . .
In [74]:
-23:
xxxxxxxxxx
1
df.salary[-23:]
Out[74]:
55 72500
56 77500
57 72500
58 144651
59 103994
60 92000
61 103750
62 109650
63 91000
64 73300
65 117555
66 63100
67 90450
68 77500
69 116450
70 78500
71 71065
72 161101
73 105450
74 104542
75 124312
76 109954
77 109646
Name: salary, dtype: int64
. . .
In [82]:
xxxxxxxxxx
1
df2 = pd.DataFrame()
. . .
In [83]:
1
1
print (type(df2))
<class ‘pandas.core.frame.DataFrame’>
. . .
In [94]:
df2[0:10] print(df2)
x
1
df2[0:10]
2
print(df2)
Empty DataFrame
Columns: []
Index: []
Question – Pandas Shell Python Example
Instructions are described in the Pandas Shell. Use Data from both the Salaries and Flights CSV Excel files to do the homework on Pandas. Please write codes in Python Pandas Shell.
