Develop Reference

pandas remove equal rows by comparing columns in two dataframes

df1 = [['tom', 10, 1.2], ['nick', 15, 1.3], ['juli', 14, 1.4]]
df1 = [['tom', 10, 1.2], ['nick', 15, 1.3], ['juli', 100, 1.4]]
When I am trying compare and remove equal using below code
diff = df1.compare(df2, align_axis=1, keep_equal=True, keep_shape=True).drop_duplicates(
keep=False).rename(index={'self': 'df1', 'other': 'df2'}, level=-1)
I am getting
I want to keep only that row which has any unequal records and remove remaining. It means only last row should be present in output not all rows like blow. Please suggest changes.

Assuming you want everything from df1 that does not matches df2
n_columns = len(df1.columns)
df1[(df1 == df2).apply(sum, axis=1).apply(lambda x: x != n_columns)]

Mapping a multiindex dataframe to another using row ID

I have two dataframes of different shape
The 'ANTENNA1' and 'ANTENNA2' in the bigger dataframe correspond to the ID columns in the smaller dataframe. I want to create merge the smaller dataframe to the bigger one so that the bigger dataframe will have '(POSITION, col1)', '(POSITION, col2)', '(POSITION, col3)' according to ANTENNA1 == ID
Edit: I tried with pd.merge but it is changing the original dataframe column values
Original:
df = pd.merge(df_main, df_sub, left_on='ANTENNA1', right_on ='id', how = 'left')
Result:
I want to keep the original dataframe columns as it is.

Assuming your first dataframe (with positions) is called df1, and the second is called df2, with your loaded data, you could just use pandas.DataFrame.merge: ( -> pd.merge(...) )
df = pd.merge(df1,df2,left_on='id', right_on='ANTENNA1')
Than you might select the df on your needed columns(col1,col2,..) to get the desired result df[["col1","col2",..]].
simple example:
# import pandas as pd
import pandas as pd
# creating dataframes as df1 and df2
df1 = pd.DataFrame({'ID': [1, 2, 3, 5, 7, 8],
'Name': ['Sam', 'John', 'Bridge',
'Edge', 'Joe', 'Hope']})
df2 = pd.DataFrame({'id': [1, 2, 4, 5, 6, 8, 9],
'Marks': [67, 92, 75, 83, 69, 56, 81]})
# merging df1 and df2 by ID
# i.e. the rows with common ID's get
# merged i.e. {1,2,5,8}
df = pd.merge(df1, df2, left_on="ID", right_on="id")
print(df)

What is wrong here in colouring the Excel sheet?

Here I need to colour 'red' for rows with Age<13 and colur 'green' for rows with Age>=13. But the final 'Report.xlsx' isn't getting coloured. What is wrong here?
import pandas as pd
data = [['tom', 10], ['nick', 12], ['juli', 14]]
df = pd.DataFrame(data, columns = ['Name', 'Age'])
df_styled = df.style.applymap(lambda x: 'background:red' if x < 13 else 'background:green', subset=['Age'])
df_styled.to_excel('Report.xlsx',engine='openpyxl',index=False)

Pandas get cell value by row NUMBER (NOT row index) and column NAME

data = [['tom', 10], ['nick', 15], ['juli', 14]]
df = pd.DataFrame(data, columns = ['Name', 'Age'], index = [7,3,9])
display(df)
df.iat[0,0]
I'd like to return the Age in first row (basically something like df.iat[0,'Age']. Expected result = 10
Thanks for your help!

df['Age'].iloc[0] works too, similar to what Chris had answered.

Use iloc and Index.get_loc:
df.iloc[0, df.columns.get_loc("Age")]
Output:
10

pandas three-way joining multiple dataframes on columns

I have 3 CSV files. Each has the first column as the (string) names of people, while all the other columns in each dataframe are attributes of that person.
How can I "join" together all three CSV documents to create a single CSV with each row having all the attributes for each unique value of the person's string name?
The join() function in pandas specifies that I need a multiindex, but I'm confused about what a hierarchical indexing scheme has to do with making a join based on a single index.

Zero's answer is basically a reduce operation. If I had more than a handful of dataframes, I'd put them in a list like this (generated via list comprehensions or loops or whatnot):
dfs = [df0, df1, df2, ..., dfN]
Assuming they have a common column, like name in your example, I'd do the following:
import functools as ft
df_final = ft.reduce(lambda left, right: pd.merge(left, right, on='name'), dfs)
That way, your code should work with whatever number of dataframes you want to merge.

You could try this if you have 3 dataframes
# Merge multiple dataframes
df1 = pd.DataFrame(np.array([
['a', 5, 9],
['b', 4, 61],
['c', 24, 9]]),
columns=['name', 'attr11', 'attr12'])
df2 = pd.DataFrame(np.array([
['a', 5, 19],
['b', 14, 16],
['c', 4, 9]]),
columns=['name', 'attr21', 'attr22'])
df3 = pd.DataFrame(np.array([
['a', 15, 49],
['b', 4, 36],
['c', 14, 9]]),
columns=['name', 'attr31', 'attr32'])
pd.merge(pd.merge(df1,df2,on='name'),df3,on='name')
alternatively, as mentioned by cwharland
df1.merge(df2,on='name').merge(df3,on='name')

This is an ideal situation for the join method
The join method is built exactly for these types of situations. You can join any number of DataFrames together with it. The calling DataFrame joins with the index of the collection of passed DataFrames. To work with multiple DataFrames, you must put the joining columns in the index.
The code would look something like this:
filenames = ['fn1', 'fn2', 'fn3', 'fn4',....]
dfs = [pd.read_csv(filename, index_col=index_col) for filename in filenames)]
dfs[0].join(dfs[1:])
With #zero's data, you could do this:
df1 = pd.DataFrame(np.array([
['a', 5, 9],
['b', 4, 61],
['c', 24, 9]]),
columns=['name', 'attr11', 'attr12'])
df2 = pd.DataFrame(np.array([
['a', 5, 19],
['b', 14, 16],
['c', 4, 9]]),
columns=['name', 'attr21', 'attr22'])
df3 = pd.DataFrame(np.array([
['a', 15, 49],
['b', 4, 36],
['c', 14, 9]]),
columns=['name', 'attr31', 'attr32'])
dfs = [df1, df2, df3]
dfs = [df.set_index('name') for df in dfs]
dfs[0].join(dfs[1:])
attr11 attr12 attr21 attr22 attr31 attr32
name
a 5 9 5 19 15 49
b 4 61 14 16 4 36
c 24 9 4 9 14 9

In python 3.6.3 with pandas 0.22.0 you can also use concat as long as you set as index the columns you want to use for the joining:
pd.concat(
objs=(iDF.set_index('name') for iDF in (df1, df2, df3)),
axis=1,
join='inner'
).reset_index()
where df1, df2, and df3 are defined as in John Galt's answer:
import pandas as pd
df1 = pd.DataFrame(np.array([
['a', 5, 9],
['b', 4, 61],
['c', 24, 9]]),
columns=['name', 'attr11', 'attr12']
)
df2 = pd.DataFrame(np.array([
['a', 5, 19],
['b', 14, 16],
['c', 4, 9]]),
columns=['name', 'attr21', 'attr22']
)
df3 = pd.DataFrame(np.array([
['a', 15, 49],
['b', 4, 36],
['c', 14, 9]]),
columns=['name', 'attr31', 'attr32']
)

This can also be done as follows for a list of dataframes df_list:
df = df_list[0]
for df_ in df_list[1:]:
df = df.merge(df_, on='join_col_name')
or if the dataframes are in a generator object (e.g. to reduce memory consumption):
df = next(df_list)
for df_ in df_list:
df = df.merge(df_, on='join_col_name')

Simple Solution:
If the column names are similar:
df1.merge(df2,on='col_name').merge(df3,on='col_name')
If the column names are different:
df1.merge(df2,left_on='col_name1', right_on='col_name2').merge(df3,left_on='col_name1', right_on='col_name3').drop(columns=['col_name2', 'col_name3']).rename(columns={'col_name1':'col_name'})

Here is a method to merge a dictionary of data frames while keeping the column names in sync with the dictionary. Also it fills in missing values if needed:
This is the function to merge a dict of data frames
def MergeDfDict(dfDict, onCols, how='outer', naFill=None):
keys = dfDict.keys()
for i in range(len(keys)):
key = keys[i]
df0 = dfDict[key]
cols = list(df0.columns)
valueCols = list(filter(lambda x: x not in (onCols), cols))
df0 = df0[onCols + valueCols]
df0.columns = onCols + [(s + '_' + key) for s in valueCols]
if (i == 0):
outDf = df0
else:
outDf = pd.merge(outDf, df0, how=how, on=onCols)
if (naFill != None):
outDf = outDf.fillna(naFill)
return(outDf)
OK, lets generates data and test this:
def GenDf(size):
df = pd.DataFrame({'categ1':np.random.choice(a=['a', 'b', 'c', 'd', 'e'], size=size, replace=True),
'categ2':np.random.choice(a=['A', 'B'], size=size, replace=True),
'col1':np.random.uniform(low=0.0, high=100.0, size=size),
'col2':np.random.uniform(low=0.0, high=100.0, size=size)
})
df = df.sort_values(['categ2', 'categ1', 'col1', 'col2'])
return(df)
size = 5
dfDict = {'US':GenDf(size), 'IN':GenDf(size), 'GER':GenDf(size)}
MergeDfDict(dfDict=dfDict, onCols=['categ1', 'categ2'], how='outer', naFill=0)

One does not need a multiindex to perform join operations.
One just need to set correctly the index column on which to perform the join operations (which command df.set_index('Name') for example)
The join operation is by default performed on index.
In your case, you just have to specify that the Name column corresponds to your index.
Below is an example
A tutorial may be useful.
# Simple example where dataframes index are the name on which to perform
# the join operations
import pandas as pd
import numpy as np
name = ['Sophia' ,'Emma' ,'Isabella' ,'Olivia' ,'Ava' ,'Emily' ,'Abigail' ,'Mia']
df1 = pd.DataFrame(np.random.randn(8, 3), columns=['A','B','C'], index=name)
df2 = pd.DataFrame(np.random.randn(8, 1), columns=['D'], index=name)
df3 = pd.DataFrame(np.random.randn(8, 2), columns=['E','F'], index=name)
df = df1.join(df2)
df = df.join(df3)
# If you have a 'Name' column that is not the index of your dataframe,
# one can set this column to be the index
# 1) Create a column 'Name' based on the previous index
df1['Name'] = df1.index
# 1) Select the index from column 'Name'
df1 = df1.set_index('Name')
# If indexes are different, one may have to play with parameter how
gf1 = pd.DataFrame(np.random.randn(8, 3), columns=['A','B','C'], index=range(8))
gf2 = pd.DataFrame(np.random.randn(8, 1), columns=['D'], index=range(2,10))
gf3 = pd.DataFrame(np.random.randn(8, 2), columns=['E','F'], index=range(4,12))
gf = gf1.join(gf2, how='outer')
gf = gf.join(gf3, how='outer')

There is another solution from the pandas documentation (that I don't see here),
using the .append
>>> df = pd.DataFrame([[1, 2], [3, 4]], columns=list('AB'))
A B
0 1 2
1 3 4
>>> df2 = pd.DataFrame([[5, 6], [7, 8]], columns=list('AB'))
A B
0 5 6
1 7 8
>>> df.append(df2, ignore_index=True)
A B
0 1 2
1 3 4
2 5 6
3 7 8
The ignore_index=True is used to ignore the index of the appended dataframe, replacing it with the next index available in the source one.
If there are different column names, Nan will be introduced.

I tweaked the accepted answer to perform the operation for multiple dataframes on different suffix parameters using reduce and i guess it can be extended to different on parameters as well.
from functools import reduce
dfs_with_suffixes = [(df2,suffix2), (df3,suffix3),
(df4,suffix4)]
merge_one = lambda x,y,sfx:pd.merge(x,y,on=['col1','col2'..], suffixes=sfx)
merged = reduce(lambda left,right:merge_one(left,*right), dfs_with_suffixes, df1)

df1 = pd.DataFrame(np.array([
['a', 5, 9],
['b', 4, 61],
['c', 24, 9]]),
columns=['name', 'attr11', 'attr12']
)
df2 = pd.DataFrame(np.array([
['a', 5, 19],
['d', 14, 16]]
),
columns=['name', 'attr21', 'attr22']
)
df3 = pd.DataFrame(np.array([
['a', 15, 49],
['c', 4, 36],
['d', 14, 9]]),
columns=['name', 'attr31', 'attr32']
)
df4 = pd.DataFrame(np.array([
['a', 15, 49],
['c', 4, 36],
['c', 14, 9]]),
columns=['name', 'attr41', 'attr42']
)
Three ways to join list dataframe
pandas.concat
dfs = [df1, df2, df3]
dfs = [df.set_index('name') for df in dfs]
# cant not run if index not unique
dfs = pd.concat(dfs, join='outer', axis = 1)
functools.reduce
dfs = [df1, df2, df3, df4]
# still run with index not unique
import functools as ft
df_final = ft.reduce(lambda left, right: pd.merge(left, right, on='name', how = 'outer'), dfs)
join
# cant not run if index not unique
dfs = [df1, df2, df3]
dfs = [df.set_index('name') for df in dfs]
dfs[0].join(dfs[1:], how = 'outer')

Joining together all three can be done using .join() function.
You have three DataFrames lets say
df1, df2, df3.
To join these into one DataFrame you can:
df = df1.join(df2).join(df3)
This is the simplest way I found to do this task.