Python--pandas数据创建

# series创建
 
import pandas as pd
import numpy as np
 
series1 = pd.Series([1, 2, 3, 4])
series1

0    1
1    2
2    3
3    4
dtype: int64

series1.index

RangeIndex(start=0, stop=4, step=1)

series1.values

array([1, 2, 3, 4], dtype=int64)

series2 = pd.Series([1, 2, 3, 4],
                   index=['a', 'b', 'c', 'd'])
series2

a    1
b    2
c    3
d    4
dtype: int64

# Create DataFrame from Dictionary using default Constructor
# 通过字典创建DataFrame
studentData = {
'name' : ['jack', 'Riti', 'Aadi'],
'age' : [34, 30, 16],
'city' : ['Sydney', 'Delhi', 'New york']
}

df = pd.DataFrame(studentData) 
df

# 创建时自定义索引
df = pd.DataFrame(studentData, index=['a', 'b', 'c'])
df

# Create DataFrame from not compatible dictionary 
# 单列字典创建DataFrame

studentAgeData = {
'Jack' : 12,
'Roma' : 13,
'Ritika' : 10,
'Aadi' : 11
}
# df = pd.DataFrame(studentAgeData)
df = pd.DataFrame(list(studentAgeData.items()), index=['a', 'b', 'c', 'd'])
df

# Create DataFrame from Dictionary and skip data 
# 跳过某列创建DataFrame

studentData = {
'name' : ['jack', 'Riti', 'Aadi'],
'age' : [34, 30, 16],
'city' : ['Sydney', 'Delhi', 'New york']
}

# Creating Dataframe from Dictionary by Skipping 2nd Item from dict 
# 跳过某列

dfObj = pd.DataFrame(studentData, columns=['name', 'city'])
dfObj

# Create DataFrame from Dictionary with different Orientation 
# 不同方向
studentData = {
'name' : ['jack', 'Riti', 'Aadi'],
'age' : [34, 30, 16],
'city' : ['Sydney', 'Delhi', 'New york']
}

# Create dataframe from dic and make keys, index in dataframe
dfObj = pd.DataFrame.from_dict(studentData, orient='index')
dfObj

# Create DataFrame from nested Dictionary 
# 包含嵌套的字典
studentData = { 
}
0 : {
'name' : 'Aadi',
'age' : 16,
'city' : 'New york'
    },
1 : {
'name' : 'Jack',
'age' : 34,
'city' : 'Sydney'
    },
2 : {
'name' : 'Riti',
'age' : 30,
'city' : 'Delhi'
    }

# Create dataframe from nested dictionary 
# 包含嵌套的字典

dfObj = pd.DataFrame(studentData)
dfObj

# Transpose dataframe object 
# 行列转换

dfObj = dfObj.transpose()
dfObj