Just like dictionary of Python:

import pandas as pd

pd.Series(data = mylist, index = labelList)
pd.Series(myDict)

DataFrames are the workhorse of pandas and are directly inspired by the R programming language. We can think of a DataFrame as a bunch of Series objects put together to share the same index. Let’s use pandas to explore this topic!

import pandas as pd
import numpy as np

from numpy.random import randn
np.random.seed(101)

df = pd.DataFrame(randn(5,4),index='A B C D E'.split(),columns='W X Y Z'.split())

Let’s learn the various methods to grab data from a DataFrame

df['W']

df.W

A    2.706850
B    0.651118
C   -2.018168
D    0.188695
E    0.190794
Name: W, dtype: float64

# Pass a list of column names
df[['W','Z']]

type(df['W'])
>> pandas.core.series.Series

Features of drop function:

1. drop method’s default axis = 0 ( = the labels), so if we want to delete a column, we shall set axis = 1
   Beacuse Pandas is built on Numpy, so it use Numpy to save the data:
   for df.shape = (5,4), we have df.shape[axis = 1] = Column.
2. the drop function isn’t inplace df. If we want to do inplace, we need:
   df.drop(‘new’,axis=1,inplace=True)

df.drop('new',axis=1)

df.drop('new',axis=1,inplace=True)

Can also drop rows this way:

df.drop('E',axis=0)

df.loc['A']

W    2.706850
X    0.628133
Y    0.907969
Z    0.503826
Name: A, dtype: float64

Or select based off of position instead of label: iloc = Index Location (0 Base)

df.iloc[2]

W   -2.018168
X    0.740122
Y    0.528813
Z   -0.589001
Name: C, dtype: float64

The conditional selection usage is similar to Numpy.

cond = df > 0

1. for all data, if data > 0 then data = data, or data = NaN
2. ‘condition’ can be taken as filter

df>0

booldf = df > 0
df[booldf]
#or
df[df>0]

1. Select data after droped data of !cond(W < 0)
   

   df[df['W']>0]

2. Select the Y, X columns after droped data of !cond(W < 0)

df[df['W']>0][['Y','X']]

3. For two conditions you can use | and & with parenthesis:
   The conditions can be seen as

   1. condition table 1 | condition table 2 
   2. condition table 1 & condition table 2 

#Select labels 
df[(df['W']>0) & (df['Y'] > 1)]

Reset Index

# Reset to default 0,1...n index
# Note : 1. the current index will be listed in a new column
#      : 2. if we want to do it inplace, use df.reset_index(inplace = True)
df.reset_index()

Set New Index by A Column in Table

newind = 'CA NY WY OR CO'.split()
df['States'] = newind
df.set_index('States',inplace=True)