Pandas data processing (a)
import pandas as pd
import numpy as np
# Numpy use to generate a set of data DataFrome
df=pd.DataFrame(np.arange(16).reshape(4,4))
print(df)
–out
0 1 2 3
0 0 1 2 3
1 4 5 6 7
2 8 9 10 11
3 12 13 14 15
# We see a line, we have not specified vertical no results have emerged,
# DataFrome because it is our two-dimensional columns, resulting in a vertical row index and the index
of course, we can also specify the index value #
df=pd.DataFrame(np.arange(16,32).reshape(4,4),index=[‘a’,‘b’,‘c’,‘d’],columns=[‘w’,‘x’,‘y’,‘z’])
print(df)
–out
w x y z
a 16 17 18 19
b 20 21 22 23
c 24 25 26 27
d 28 29 30 31
#DataFrome Import Dictionary
a = { 'Id': [ '001', '002', '003'], 'name': [ 'cat', 'dog', 'wolf'], 'sex': [ 'F' , 'M', 'M']}
df=pd.DataFrame(a)
print(df)
-Out
Id name Sex
0 001 kittens female
1002 male puppy
2003 male wolf
#pandas really powerful, and this is my very favorite place
# View row index
print(df.index)
–out
RangeIndex(start=0, stop=3, step=1)
# View column index
print(df.columns)
–out
Index([‘Id’, ‘name’, ‘sex’], dtype=‘object’)
# View data
print(df.values)
-Out
[[ '001' 'cat' 'woman']
[ '002' 'dog' 'M']
[ '003' 'coyotes' 'M']]
# View type
print(type(df))
–out
<class ‘pandas.core.frame.DataFrame’>
# View the list of data types
print(type(df))
–out
Id object
name object
sex object
dtype: object
# View Data Dimensions
print(df.shape)
–out
(3, 3)
Required to display data #
print(df.head(1))
-Out
Id name Sex
0 001 female kitten
# Displays the first line the countdown
print(df.tail(1))
-Out
Id name Sex
2 003 male coyotes
# Display list information
print(df.info())
–out
<class ‘pandas.core.frame.DataFrame’>
RangeIndex: 3 entries, 0 to 2
Data columns (total 3 columns):
Column Non-Null Count Dtype
0 Id 3 non-null object
1 name 3 non-null object
2 sex 3 non-null object
dtypes: object(3)
memory usage: 200.0+ bytes
None
