Pandas: Common 1

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astype()¶

info()¶

nunique()¶

nunique()¶

value_sort()¶

rank()¶

Convert data type in with astype() method¶

In [68]:

import pandas as pd
df = pd.read_csv("test5.csv")
df

Out[68]:

	number	name	species
0	1.0	joe	human
1	2.0	john	human
2	3.0	mike	human
3	4.0	didi	cat
4	5.0	aaron	human
5	6.0	boo	dog
6	7.0	ziggy	dog
7	8.0	balou	gorilla

In [69]:

df.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 8 entries, 0 to 7
Data columns (total 3 columns):
 #   Column   Non-Null Count  Dtype  
---  ------   --------------  -----  
 0   number   8 non-null      float64
 1   name     8 non-null      object 
 2   species  8 non-null      object 
dtypes: float64(1), object(2)
memory usage: 320.0+ bytes

In [70]:

import pandas as pd
df = pd.read_csv("test5.csv")
df["number"].astype("int")
#no inplace parameter, so assign it back to itself
#df = df["number"].astype("int")
#df.info()
#After converted, it is becoming a Series.
df["number"] = df["number"].astype("int")
df.info()
df[["name","number"]]

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 8 entries, 0 to 7
Data columns (total 3 columns):
 #   Column   Non-Null Count  Dtype 
---  ------   --------------  ----- 
 0   number   8 non-null      int64 
 1   name     8 non-null      object
 2   species  8 non-null      object
dtypes: int64(1), object(2)
memory usage: 320.0+ bytes

Out[70]:

	name	number
0	joe	1
1	john	2
2	mike	3
3	didi	4
4	aaron	5
5	boo	6
6	ziggy	7
7	balou	8

In [71]:

import pandas as pd
df = pd.read_csv("test5.csv")
df["number"].astype("int")
#no inplace parameter, so assign it back to itself
df = df["number"].astype("int")
#df.info()
#After converted, it is becoming a Series.
#watch out this is reassigning back to entire dataframe
df

Out[71]:

0    1
1    2
2    3
3    4
4    5
5    6
6    7
7    8
Name: number, dtype: int64

In [ ]:

using nunique() method like select count(distinct COLUMN ) in sql¶

In [72]:

import pandas as pd
df = pd.read_csv("test5.csv")
#df["species"].nunique()
df["species"].nunique()

Out[72]:

4

unique() to list the category ... similar to the following of Categorical at the pd level. at df level uses nunique() (n for number) and unique() methods¶

In [73]:

import pandas as pd
df = pd.read_csv("test5.csv")
#df["species"].nunique()
df["species"].unique()

Out[73]:

array(['human', 'cat', 'dog', 'gorilla'], dtype=object)

using Categorical to list out the category like select distinct() in sql¶

In [74]:

import pandas as pd
df = pd.read_csv("test5.csv")
pd.Categorical(df["species"].unique())

Out[74]:

[human, cat, dog, gorilla]
Categories (4, object): [cat, dog, gorilla, human]

usign .value_sort() like ORDER BY in sql¶

need at least one parameter which is the column. This can be multi-columns sort. It supports inplace.¶

In [80]:

import pandas as pd
df = pd.read_csv("test5.csv")
df.sort_values(by = "name", ascending ="False")

Out[80]:

	number	name	species
4	5.0	aaron	human
7	8.0	balou	gorilla
5	6.0	boo	dog
3	4.0	didi	cat
0	1.0	joe	human
1	2.0	john	human
2	3.0	mike	human
6	7.0	ziggy	dog

In [87]:

#multicolumns sort with ascending true or false
df.sort_values(by=["name", "species"], ascending=[False,True])

Out[87]:

	number	name	species
6	7.0	ziggy	dog
2	3.0	mike	human
1	2.0	john	human
0	1.0	joe	human
3	4.0	didi	cat
5	6.0	boo	dog
7	8.0	balou	gorilla
4	5.0	aaron	human

using .rank() like TopN Analysis in RANK() in SQL¶

In [89]:

import pandas as pd
df = pd.read_csv('test5.csv')
#rank all columns individually from the dataframe. Interesting that none numerical can be ranked as well
df.rank()

Out[89]:

	number	name	species
0	1.0	5.0	6.5
1	2.0	6.0	6.5
2	3.0	7.0	6.5
3	4.0	4.0	1.0
4	5.0	1.0	6.5
5	6.0	3.0	2.5
6	7.0	8.0	2.5
7	8.0	2.0	4.0

In [92]:

#rank individual column provided by the dataframe
df["number"].rank(ascending=False)

Out[92]:

0    8.0
1    7.0
2    6.0
3    5.0
4    4.0
5    3.0
6    2.0
7    1.0
Name: number, dtype: float64

In [99]:

#this works like TopN Analysis in sql where only 3 top to be queried, inplaced it into original column
df["number"] = df["number"].rank(ascending=True).head(3)
#drop out the ones that not selected from presenting to the output
df.dropna()

Out[99]:

	number	name	species
0	1.0	joe	human
1	2.0	john	human
2	3.0	mike	human

In [ ]: