Lambdas function on multiple columns - pandas

I am trying to extract only number from multiple columns in my pandas data.frame.
I am able to do so one-by-one columns however I would like to perform this operation simultaneously to multiple columns
My reproduced example:
import pandas as pd
import re
import numpy as np
import seaborn as sns
df = sns.load_dataset('diamonds')
# Create columns one again
df['clarity2'] = df['clarity']
df.head()
df[['clarity', 'clarity2']].apply(lambda x: x.str.extract(r'(\d+)'))


If you want a tuple
cols = ['clarity', 'clarity2']
tuple(df[col].str.extract(r'(\d+)') for col in cols)
If you want a list
cols = ['clarity', 'clarity2']
[df[col].str.extract(r'(\d+)') for col in cols]
adding them to the original data
df['digit1'], df['digit2'] = [df[col].str.extract(r'(\d+)') for col in cols]

Related

How to subset a dataframe, groupby and export the dataframes as multiple sheets of a one excel file in Python

Python newbie here
In the dataset below:
import pandas as pd
import numpy as np
data = {'Gender':['M','M','M','M','F','F','F','F','M','M','M','M','F','F','F','F'],
'Location':['NE','NE','NE','NE','SW','SW','SW','SW','SE','SE','SE','SE','NC','NC','NC','NC'],
'Type':['L','L','L','L','L','L','L','L','R','R','R','R','R','R','R','R'],
'PDP':['<10','<10','<10','<10',10,10,10,10,20,20,20,20,'>20','>20','>20','>20'],
'PDP_code':[1,1,1,1,2,2,2,2,3,3,3,3,4,4,4,4],
'diff':[-1,-1,-1,-1,0,0,0,0,1,1,1,1,3,3,3,3],
'series':[1,2,4,8,1,2,4,8,1,2,4,8,1,2,4,8],
'Revenue_YR1':[1150.78,1162.34,1188.53,1197.69,2108.07,2117.76,2129.48,1319.51,1416.87,1812.54,1819.57,1991.97,2219.28,2414.73,2169.91,2149.19],
'Revenue_YR2':[250.78,262.34,288.53,297.69,308.07,317.7,329.81,339.15,346.87,382.54,369.59,399.97,329.28,347.73,369.91,349.12],
'Revenue_YR3':[240.18,232.14,258.53,276.69,338.07,307.74,359.16,339.25,365.87,392.48,399.97,410.75,429.08,448.39,465.15,469.33],
'Revenue_YR4':[270.84,282.14,298.53,306.69,318.73,327.47,369.63,389.59,398.75,432.18,449.78,473.55,494.85,509.39,515.52,539.23],
'Revenue_YR5':[251.78,221.34,282.53,272.69,310.07,317.7,329.81,333.15,334.87,332.54,336.59,339.97,329.28,334.73,336.91,334.12],
'Revenue_YR6':[240.18,232.14,258.53,276.69,338.07,307.74,359.16,339.25,365.87,392.48,399.97,410.75,429.08,448.39,465.15,469.33],
'Revenue_YR7':[27.84,28.14,29.53,30.69,18.73,27.47,36.63,38.59,38.75,24.18,24.78,21.55,13.85,9.39,15.52,39.23],
'Revenue_YR8':[279.84,289.14,299.53,309.69,318.73,327.47,336.63,398.59,398.75,324.18,324.78,321.55,333.85,339.39,315.52,319.23],
}
df = pd.DataFrame(data,columns = ['Gender','Location','Type','PDP','PDP_code','diff','series',
'Revenue_YR1','Revenue_YR2','Revenue_YR3','Revenue_YR4','Revenue_YR5','Revenue_YR6',
'Revenue_YR7','Revenue_YR8'])
df.head(5)
I want a pythonic way of doing the following :
subset df into 4 dataframes / lists based on unique Location resulting in NE,SW,SE & NC dataframes
aggregating all the Revenue_YR columns while GroupBy series and PDP_code columns and export all the aggregated dataframes (NE,SW,SE & NC) as multiple sheets of one xlsx file
My attempt
### this code returns output of 1 df instead of 4 dfs, I need help aggregating each of the 4 dataframes and export them to 4 sheets of 12312021_output.xlsx
for i, part_df in df.groupby('Location'):
part_df.groupby(['series','PDP_code'])[['Revenue_YR1', 'Revenue_YR2','Revenue_YR3',
'Revenue_YR4', 'Revenue_YR5', 'Revenue_YR6', 'Revenue_YR7']].mean().unstack().style.background_gradient(cmap='Blues').to_excel('12312021_output.xlsx')
Please share your code.

You can use pandas.ExcelWriter, and your loop (which I improved slightly for readability):
import pandas as pd
with pd.ExcelWriter("output.xlsx") as writer:
cols = df.filter(like='Revenue_YR').columns
for g, d in df.groupby('Location'):
(d.groupby(['series','PDP_code'])[cols].mean().unstack()
.style.background_gradient(cmap='Blues')
).to_excel(writer, sheet_name=g)

Iterate Over columns in pandas dataframe using list comprehension

I would like to pefrom following operation using list comprehension:
import numpy as np
import pandas as pd
import seaborn as sns
df = sns.load_dataset('tips')
df.head()
for i in df.columns:
print(df.loc[:, i].is_unique)
Using [x.is_unique for x in df.loc[:, i] for i in df.columns] does not work

Use Series.is_unique with one for:
out = [df[i].is_unique for i in df.columns]
Alternative solution (I prefer first for more clear iterate by columns):
out = [df[i].is_unique for i in df]

How can I get an interpolated value from a Pandas data frame?

I have a simple Pandas data frame with two columns, 'Angle' and 'rff'. I want to get an interpolated 'rff' value based on entering an Angle that falls between two Angle values (i.e. between two index values) in the data frame. For example, I'd like to enter 3.4 for the Angle and then get an interpolated 'rff'. What would be the best way to accomplish that?
import pandas as pd
data = [[1.0,45.0], [2,56], [3,58], [4,62],[5,70]] #Sample data
s= pd.DataFrame(data, columns = ['Angle', 'rff'])
print(s)
s = s.set_index('Angle') #Set 'Angle' as index
print(s)
result = s.at[3.0, "rff"]
print(result)

You may use numpy:
import numpy as np
np.interp(3.4, s.index, s.rff)
#59.6

You could use numpy for this:
import numpy as np
import pandas as pd
data = [[1.0,45.0], [2,56], [3,58], [4,62],[5,70]] #Sample data
s= pd.DataFrame(data, columns = ['Angle', 'rff'])
print(s)
print(np.interp(3.4, s.Angle, s.rff))
>>> 59.6

Distribution probabilities for each column data frame, in one plot

I am creating probability distributions for each column of my data frame by distplot from seaborn library sns.distplot(). For one plot I do
x = df['A']
sns.distplot(x);
I am trying to use the FacetGrid & Map to have all plots for each columns at once
in this way. But doesn't work at all.
g = sns.FacetGrid(df, col = 'A','B','C','D','E')
g.map(sns.distplot())

I think you need to use melt to reshape your dataframe to long format, see this MVCE:
df = pd.DataFrame(np.random.random((100,5)), columns = list('ABCDE'))
dfm = df.melt(var_name='columns')
g = sns.FacetGrid(dfm, col='columns')
g = (g.map(sns.distplot, 'value'))
Output:
From seaborn 0.11.2 it is not recommended to use FacetGrid directly. Instead, use sns.displot for figure-level plots.
np.random.seed(2022)
df = pd.DataFrame(np.random.random((100,5)), columns = list('ABCDE'))
dfm = df.melt(var_name='columns')
g = sns.displot(data=dfm, x='value', col='columns', col_wrap=3, common_norm=False, kde=True, stat='density')

You're getting this wrong on two levels.
Python syntax.
FacetGrid(df, col = 'A','B','C','D','E') is invalid, because col gets set to A and the remaining characters are interpreted as further arguments. But since they are not named, this is invalid python syntax.
Seaborn concepts.
Seaborn expects a single column name as input for the col or row argument. This means that the dataframe needs to be in a format that has one column which determines to which column or row the respective datum belongs.
You do not call the function to be used by map. The idea is of course that map itself calls it.
Solutions:
Loop over columns:
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
df = pd.DataFrame(np.random.randn(14,5), columns=list("ABCDE"))
fig, axes = plt.subplots(ncols=5)
for ax, col in zip(axes, df.columns):
sns.distplot(df[col], ax=ax)
plt.show()
Melt dataframe
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
df = pd.DataFrame(np.random.randn(14,5), columns=list("ABCDE"))
g = sns.FacetGrid(df.melt(), col="variable")
g.map(sns.distplot, "value")
plt.show()

You can use the following:
# listing dataframes types
list(set(df.dtypes.tolist()))
# include only float and integer
df_num = df.select_dtypes(include = ['float64', 'int64'])
# display what has been selected
df_num.head()
# plot
df_num.hist(figsize=(16, 20), bins=50, xlabelsize=8, ylabelsize=8);

I think the easiest approach is to just loop the columns and create a plot.
import numpy as np
improt pandas as pd
import matplotlib.pyplot as plt
df = pd.DataFrame(np.random.random((100,5)), columns = list('ABCDE'))
for col in df.columns:
hist = df[col].hist(bins=10)
print("Plotting for column {}".format(col))
plt.show()

applying random distribution in each row of data frame

I have the following dataframe
import numpy as np
import pandas as pd
import scipy as sc
import scipy.stats as sct
d= {'col1': [1, 2,5,0.6], 'col2': [3, 4,1,0.8]}
df = pd. DataFrame(data=d)
I want to add two new column in that dataframe but the element of two new columns are the random poisson distribution of col1 and col2
I used the following coding to generate the new columns (col3 and col4).
df ['col3'] = int(sct.poisson.rvs(df.col1,size=1))
df ['col4'] = int(sct.poisson.rvs(df.col2,size=1))
This is the closet example of my dataframe which is quite huge and it contains 3,800,000 rows.
I can generate it using for loop. it took me too long time.
How can generate random poisson distribution based on dataframe without using loop?
Thanks
Zep

Try just using:
df['col3'] = sct.poisson.rvs(df.col1)
df['col4'] = sct.poisson.rvs(df.col2)