pandas.DataFrame

• Basic concepts
• Create method
• • By an array of / A dictionary of lists
  • from Series A dictionary made up of
  • Create directly from a two-dimensional array
  • A list of dictionaries
  • A dictionary made up of dictionaries

Basic concepts

DataFrame It's a Tabular form Data structure of , Contains an ordered set of columns , The value type of the column can be The number 、 character string 、 Boolean value etc. .Dataframe The data in is stored in one or more two-dimensional blocks , It's not a list 、 Dictionary or one-dimensional array structure

# Dataframe data structure 
# Dataframe It's a tabular data structure ," 2D array with labels "
# Dataframe with index（ Row labels ） and columns（ Column labels ）
import numpy
import pandas
data = {
"name":[" Takahashi Li Yi "," Xiaoyuan is so beautiful "],"age":[20,21],"gender":[" Woman "," Woman "]}
frame = pandas.DataFrame(data)
print(frame) # View the data , The data type is dataframe
print(frame.index) # .index View row labels 
print(frame.columns) # .columns View column labels 
print(frame.values) # .values Check the value , The data type is ndarray

Create method

By an array of / A dictionary of lists

import numpy
import pandas
# The length of the dictionary value must be consistent ！
data1 = {
"a":[1,2,3],"b":[3,4,5]}
data2 = {
"one":numpy.random.rand(3),"two":numpy.random.rand(3)}
print(data1)
print(data2)
# Create method :pandas.Dataframe()
d1 = pandas.DataFrame(data1)
d2 = pandas.DataFrame(data2)
# By an array of / Create a dictionary of lists Dataframe,columns For the dictionary key,index Is the default numeric label 
print(d1)
print(d2)
# columns Parameters ： You can re specify the order of columns , The format is list, If the column does not exist in the existing data ( such as 'd'), Then NaN value 
df1 = pandas.DataFrame(data1,columns = ["b","a","d"])
print(df1)
# columns Parameters ： When reassigning , The number of columns can be less than the original data 
df2 = pandas.DataFrame(data2,columns = ["two"])
print(df2)
# index Parameters ： Redefinition index, The format is list, The length must be consistent 
df2 = pandas.DataFrame(data2,index = ["f1","f2","f3"])
print(df2)

from Series A dictionary made up of

import numpy
import pandas
# from Seris A dictionary made up of establish Dataframe,columns For the dictionary key,index by Series The label of 
# If Series No label specified , Is the default numeric label 
# No settings index Of Series
data1 = {
'one':pandas.Series(numpy.random.rand(2)),
'two':pandas.Series(numpy.random.rand(3))}
# Set up index Of Series
data2 = {
'one':pandas.Series(numpy.random.rand(2),
index = ['a','b']),'two':pandas.Series(numpy.random.rand(3),index = ['a','b','c'])}
print(data1)
print(data2)
# Series Can be of different lengths , Generated Dataframe There will be NaN value 
df1 = pandas.DataFrame(data1)
df2 = pandas.DataFrame(data2)
print(df1)
print(df2)

Create directly from a two-dimensional array

import numpy
import pandas
# Create directly from a two-dimensional array Dataframe, Get the result data of the same shape 
# If you don't specify index and columns, Both return the default number format 
ar = numpy.random.rand(9).reshape(3,3)
print(ar)
df1 = pandas.DataFrame(ar)
df2 = pandas.DataFrame(ar,index = ['a', 'b', 'c'], columns = ['one','two','three'])
print(df1)
print(df2)
# index and colunms The specified length is consistent with the original array 

A list of dictionaries

import numpy
import pandas
data = [{
"one":1,"two":2},{
"one":5,"two":10,"three":20}]
print(data)
# Create a list of dictionaries Dataframe,columns For the dictionary key,index If it is not specified, it is the default array label 
# colunms and index Parameter to reassign the corresponding column and row labels respectively 
df1 = pandas.DataFrame(data)
df2 = pandas.DataFrame(data,index = ["a","b"])
df3 = pandas.DataFrame(data,columns = ["one","two"])
print(df1)
print(df2)
print(df3)

A dictionary made up of dictionaries

import numpy
import pandas
data = {
'Jack':{
'math':90,'english':89,'art':78},
'Marry':{
'math':82,'english':95,'art':92},
'Tom':{
'math':78,'english':67}}
# A dictionary made up of dictionaries Dataframe,columns For the dictionary key,index For sub dictionary key
df1 = pandas.DataFrame(data)
print(df1)
# columns Parameters can increase and decrease existing columns , If a new column appears , The value is NaN
df2 = pandas.DataFrame(data, columns = ['Jack','Tom','Bob'])
# index It's different here , It doesn't change the original index
# If you point to a new label , The value is NaN
df3 = pandas.DataFrame(data, index = ['a','b','c'])
print(df2)
print(df3)