[Python learning part 3] Convert pandas DataFrame, Series, and standard List to each other

At the beginning

--I want to convert DataFrame, Series, and standard list used in pandas to each other. --When using data processed by pandas in other libraries, it may only support list, so don't worry.

Work environment

• OS：macOS Catalina ver10.15.6 --Code editor: jupyter lab --Language: python3

Reference site

-Convert pandas.DataFrame, Series and Python standard list to each other -Pandas User Guide "Indexing and Selecting Data" (Official Document Japanese Translation)

• pandas documentation

Actually try

--Convert from list to dataframe, series --Convert from series to list --Convert from dataframe to list, series

Convert from list to dataframe, series

list→series

#Import pandas and rename it to pd
import pandas as pd
#View from Create List of Standard Library
l_1d = [0, 1, 2]
l_1d

#[0, 1, 2]

#Next, type convert list to series
s = pd.Series(l_1d)
s

#0    0
#1    1
#2    2
#dtype: int64
#If nothing is specified, index is automatically determined from 0

#Next, convert from list to series type while specifying index
s = pd.Series(l_1d, index=['row1', 'row2', 'row3'])
s

#row1    0
#row2    1
#row3    2
#dtype: int64

#You can also specify up to index at once
#It has nothing to do with this article, but it may be used for other purposes
s = pd.Series({"row1":0,"row2":1,"row3":2})
s

#row1    0
#row2    1
#row3    2
#dtype: int64

list→dataframe

#Prepare a two-dimensional array for conversion to a data frame
l_2d = [[0, 1, 2], [3, 4, 5]]

df = pd.DataFrame(l_2d)
df

#	1	2
#0	0	1	2
#1	3	4	5

#index,It is also possible to create a dataframe by specifying both columns
df = pd.DataFrame(l_2d,
                  index=['row1', 'row2'],
                  columns=['col1', 'col2', 'col3'])
df

#	col1	col2	col3
#row1	0	1	2
#row2	3	4	5

#Index at once,You can also specify up to column
#It has nothing to do with this article, but it may be used for other purposes
df = pd.DataFrame([[1,2,3],[4,5,6]],["row1","row2"],["col1","col2","col3"])
df

#	col1	col2	col3
#row1	1	2	3
#row2	4	5	6

Convert from series to list

series→list

#First, create a series
s = pd.Series([0, 1, 2])
print(s)

# 0    0
# 1    1
# 2    2
# dtype: int64

#Get only the value of series as list type
l_1d = s.values.tolist()
print(l_1d)

#[0, 1, 2]

Convert from dataframe to list, series

dataframe→list

#Create dataframe
df = pd.DataFrame([[0, 1, 2], [3, 4, 5]])
print(df)

#   0  1  2
#0  0  1  2
#1  3  4  5

#Get only the value of dataframe as list type
#index and columns are ignored
l_2d = df.values.tolist()
print(l_2d)

#[[0, 1, 2], [3, 4, 5]]

#If you get the dataframe as a list type, it becomes a two-dimensional array.
#How to convert 2D to 1D array
#It doesn't matter, but if there is, maybe I'll use it someday?

#I will use numpy so I will import it
import numpy as np
#Convert from standard library list type to array type
#Both are arrays
arr_list = np.array(l_2d)
arr_list.flatten().tolist()

#[0, 1, 2, 3, 4, 5]

dataframe→series

#Create a dataframe
df = pd.DataFrame([[0, 1, 2], [3, 4, 5]],["row1","row2"],["col1","col2","col3"])
print(df)

#      col1  col2  col3
#row1     0     1     2
#row2     3     4     5

#Get column as series
df_toSer = df["col1"]
df_toSer

#row1    0
#row2    3
#Name: col1, dtype: int64

#Get index as series
df_toSer2 = df.iloc[0]
df_toSer2

#col1    0
#col2    1
#col3    2
#Name: row1, dtype: int64

At the end

This is the site that I referred to this time. -Convert pandas.DataFrame, Series and Python standard list to each other -Pandas User Guide "Indexing and Selecting Data" (Official Document Japanese Translation)

• pandas documentation