useful_codes_techniques/pandas_examples.py at master · 0Navin0/useful_codes_techniques · GitHub

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## Pandas: merging, joining and concatenating DataFrames
## df.concat() , df.join(), df.merge()
import pandas as pd
import numpy as np

#-----------df.concat()-------------------------
# Define a dictionary containing employee data
data1 = {'Name':['Jai', 'Princi', 'Gaurav', 'Anuj'],
        'Age':[27, 24, 22, 32],
        'Address':['Nagpur', 'Kanpur', 'Allahabad', 'Kannuaj'],
        'Qualification':['Msc', 'MA', 'MCA', 'Phd']}

# Define a dictionary containing employee data
data2 = {'Name':['Abhi', 'Ayushi', 'Dhiraj', 'Hitesh'],
        'Age':[17, 14, 12, 52],
        'Address':['Nagpur', 'Kanpur', 'Allahabad', 'Kannuaj'],
        'Qualification':['Btech', 'B.A', 'Bcom', 'B.hons']}

# Convert the dictionary into DataFrame
df = pd.DataFrame(data1,index=[0, 1, 2, 3])

# Convert the dictionary into DataFrame
df1 = pd.DataFrame(data2, index=[4, 5, 6, 7])

print(df, "\n\n", df1)

# using a .concat() method
frames = [df, df1]

res1 = pd.concat(frames)

# next, what if two dfs don't have the same keys??

data1 = {'Name':['Jai', 'Princi', 'Gaurav', 'Anuj'],
        'Address':['Nagpur', 'Kanpur', 'Allahabad', 'Kannuaj'],
        'Qualification':['Msc', 'MA', 'MCA', 'Phd'],
        'new_axis':[4,5,6,7]}

data2 = {'Name':['Abhi', 'Ayushi', 'Dhiraj', 'Hitesh'],
        'Age':[17, 14, 12, 52],
        'Address':['Nagpur', 'Kanpur', 'Allahabad', 'Kannuaj'],
        'Qualification':['Btech', 'B.A', 'Bcom', 'B.hons']}

df = pd.DataFrame(data1,index=[0, 1, 2, 3])
df1 = pd.DataFrame(data2, index=[4, 5, 6, 7])
frames = [df, df1]
#res1 = pd.concat(frames)
res1= pd.concat(frames, sort=False)
## blank places get filled by NaN during concatenation.

##concatenating by some logic on axes of DataFrames

data1 = {'Name':['Jai', 'Princi', 'Gaurav', 'Anuj'],
        'Age':[27, 24, 22, 32],
        'Address':['Nagpur', 'Kanpur', 'Allahabad', 'Kannuaj'],
        'Qualification':['Msc', 'MA', 'MCA', 'Phd'],
        'Mobile No': [97, 91, 58, 76]}

data2 = {'Name':['Gaurav', 'Anuj', 'Dhiraj', 'Hitesh'],
        'Age':[22, 32, 12, 52],
        'Address':['Allahabad', 'Kannuaj', 'Allahabad', 'Kannuaj'],
        'Qualification':['MCA', 'Phd', 'Bcom', 'B.hons'],
        'Salary':[1000, 2000, 3000, 4000]}

df = pd.DataFrame(data1,index=[0, 1, 2, 3])
# what happens if you change the index of the first two entries below??
# the concatenation join=inner will give empty intersection.
df1 = pd.DataFrame(data2, index=[2, 3, 6, 7])
print(df, "\n\n", df1)
# Now we set join = inner for intersection of dataframe

# applying concat with axes,with the intersection defined by the content of axis=1
res2 = pd.concat([df, df1], axis=1, join='inner')

# what happens when you don't give sort=False....check.
# join = outer ....default
res3 = pd.concat([df, df1], axis=1, sort=False)

# concatenating along the axes of df, using join_axes argument.
res4 = pd.concat([df, df1], axis=1, join_axes=[df.index])
#---------------------------------------------

#--------------df.append()-----------------
# append is same as concat along axis=0
# axis=0 means the index column
res=pd.concat([df,df1],axis=0,sort=False)
res=df.append(df1)

# appending by ignoring the indices of the two dfs
res = pd.concat([df, df1], ignore_index=True)