Some pandas tricks 2
Теги:
Create a pandas DataFrame from multiple dicts
>>> person1 = {'type': 01, 'name': 'Jhon', 'surname': 'Smith', 'phone': '555-1234'}
>>> person2 = {'type': 01, 'name': 'Jannette', 'surname': 'Jhonson', 'credit': 1000000.00}
>>> animal1 = {'type': 03, 'cname': 'cow', 'sciname': 'Bos....', 'legs': 4, 'tails': 1 }
>>> pd.DataFrame([person1])
name phone surname type
0 Jhon 555-1234 Smith 1
>>> pd.DataFrame([person1, person2])
credit name phone surname type
0 NaN Jhon 555-1234 Smith 1
1 1000000 Jannette NaN Jhonson 1
>>> pd.DataFrame.from_dict([person1, person2])
credit name phone surname type
0 NaN Jhon 555-1234 Smith 1
1 1000000 Jannette NaN Jhonson 1
How to convert index of a pandas dataframe into a column
df['index1'] = df.index
# or
df = df.reset_index(level=0)
How to get a value from a cell of a dataframe
In [3]: sub_df
Out[3]:
A B
2 -0.133653 -0.030854
In [4]: sub_df.iloc[0]
Out[4]:
A -0.133653
B -0.030854
Name: 2, dtype: float64
In [5]: sub_df.iloc[0]['A']
Out[5]: -0.13365288513107493
Assign new columns to a DataFrame
>>> df = pd.DataFrame({'temp_c': [17.0, 25.0]},
index=['Portland', 'Berkeley'])
>>> df
temp_c
Portland 17.0
Berkeley 25.0
>>> df.assign(temp_f=lambda x: x.temp_c * 9 / 5 + 32)
temp_c temp_f
Portland 17.0 62.6
Berkeley 25.0 77.0
Chart Visualization in pandas
Pandas DataFrame Groupby two columns and get counts
>>> df = pd.DataFrame([[1.1, 1.1, 1.1, 2.6, 2.5, 3.4,2.6,2.6,3.4,3.4,2.6,1.1,1.1,3.3], list('AAABBBBABCBDDD'), [1.1, 1.7, 2.5, 2.6, 3.3, 3.8,4.0,4.2,4.3,4.5,4.6,4.7,4.7,4.8], ['x/y/z','x/y','x/y/z/n','x/u','x','x/u/v','x/y/z','x','x/u/v/b','-','x/y','x/y/z','x','x/u/v/w'],['1','3','3','2','4','2','5','3','6','3','5','1','1','1']]).T
>>> df.columns = ['col1','col2','col3','col4','col5']
>>> df
col1 col2 col3 col4 col5
0 1.1 A 1.1 x/y/z 1
1 1.1 A 1.7 x/y 3
2 1.1 A 2.5 x/y/z/n 3
3 2.6 B 2.6 x/u 2
4 2.5 B 3.3 x 4
5 3.4 B 3.8 x/u/v 2
6 2.6 B 4 x/y/z 5
7 2.6 A 4.2 x 3
8 3.4 B 4.3 x/u/v/b 6
9 3.4 C 4.5 - 3
10 2.6 B 4.6 x/y 5
11 1.1 D 4.7 x/y/z 1
12 1.1 D 4.7 x 1
13 3.3 D 4.8 x/u/v/w 1
In [11]: df.groupby(['col5', 'col2']).size()
Out[11]:
col5 col2
1 A 1
D 3
2 B 2
3 A 3
C 1
4 B 1
5 B 2
6 B 1
dtype: int64
In [12]: df.groupby(['col5', 'col2']).size().groupby(level=1).max()
Out[12]:
col2
A 3
B 2
C 1
D 3
dtype: int64
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