/ Python And R Data science skills: 55 Opreators ond mis functions Part01

Saturday 10 February 2018

55 Opreators ond mis functions Part01

https://vlrtraining.com/courses/python-data-science-beginner-tutorial 55 Opreators ond mis functions
In [25]:
import pandas as pd
df = pd.DataFrame({'col1':[1,2,3,4],'col2':[444,555,666,444],'col3':['abc','def','ghi','xyz']})
In [5]:
df.head(n=1)
Out[5]:
col1 col2 col3
0 1 444 abc
In [3]:
df
Out[3]:
col1 col2 col3
0 1 444 abc
1 2 555 def
2 3 666 ghi
3 4 444 xyz
In [7]:
df['col2'].unique()
Out[7]:
array([444, 555, 666], dtype=int64)
In [8]:
df['col2'].nunique()
Out[8]:
3
In [9]:
newdf = df[(df['col1']>2) & (df['col2']==444)]
In [12]:
df[(df["col2"]>450) & (df["col2"]<600)]
Out[12]:
col1 col2 col3
1 2 555 def
In [13]:
def doub(x):
    return x*2
In [15]:
doub(9)
Out[15]:
18
In [16]:
df
Out[16]:
col1 col2 col3
0 1 444 abc
1 2 555 def
2 3 666 ghi
3 4 444 xyz
In [17]:
df['col2'].apply(doub)
Out[17]:
0     888
1    1110
2    1332
3     888
Name: col2, dtype: int64
In [18]:
df['col3'].apply(len)
Out[18]:
0    3
1    3
2    3
3    3
Name: col3, dtype: int64
In [19]:
df['col2'].sum()
Out[19]:
2109
In [26]:
#del df['col1']
df
Out[26]:
col1 col2 col3
0 1 444 abc
1 2 555 def
2 3 666 ghi
3 4 444 xyz
In [29]:
df.index
df.columns
Out[29]:
Index(['col1', 'col2', 'col3'], dtype='object')
In [34]:
df.sort_values('col3',ascending=False)
Out[34]:
col1 col2 col3
3 4 444 xyz
2 3 666 ghi
1 2 555 def
0 1 444 abc
In [35]:
df.isnull()
Out[35]:
col1 col2 col3
0 False False False
1 False False False
2 False False False
3 False False False

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