We Keep Coding

Smart way to plot (very skewed) wealth distribution across different years?

I have multiple dataframes (different years) that looks like the following dataframe. Each dataframe contains the share of wealth each id holds (across equally distributed 1000 units of x-axis bins. So for instance, if there are 4,000,000 individuals, each bin will represent the sum of 4,000 individuals in descending order). What I want is to plot this in one chart. I am lacking creatibity as to what is the best to way to show these very skewed wealth distribution across different years...
When i look at my dataframe from year 2021, the top 0.1 holds 92% of all wealth. So when I plot it using a bar chart, it looks like just one straight vertical line, and if i use a line chart, it is a L-shaped graph. I was thinking maybe i should have different x-axis bin width, as in, insteady of using 1000 equal sized bins on the a-axis, maybe the top 0.1%, top 0.1-0.5%, top 0.5-1%, 1-5%, 5-10%, 10-20%,... etc.
If anyone has a good idea, i'd really really appreciate it!
x wealth_share_2016
1 0.33430437283205316
2 0.08857907028903435
3 0.05827083476711605
4 0.03862747269456592
5 0.034995688078949164
6 0.025653645763917113
7 0.021026627708501285
8 0.018026751734878957
9 0.01642864468243111
10 0.015728925648574896
11 0.013588290634843092
12 0.01227954727973525
13 0.011382643296594532
14 0.010141965617682762
15 0.008819245941582449
..
1000 0.000000000011221421
x wealth_share_2017
0.0 0.901371131515615
1.0 0.029149650261610725
2.0 0.01448219525035078
3.0 0.00924941242097224
4.0 0.006528547368042855
5.0 0.004915282901262396
6.0 0.0038227195841958007
7.0 0.003202422960559232
8.0 0.0027194902152005056
9.0 0.002256081738439025
10.0 0.001913906326353021
11.0 0.001655920262049755
12.0 0.001497315358785623
13.0 0.0013007783674694787
14.0 0.0011483994993211357
15.0 0.0010006446573525651
16.0 0.0009187314949837794
17.0 0.0008060306765341464
18.0 0.0007121683663280601
19.0 0.0006479765506981805
20.0 0.0006209618807503557
21.0 0.0005522371927723867
22.0 0.0004900821167110386
23.0 0.0004397140637940455
24.0 0.00039311806560654995
25.0 0.0003568253540177216
26.0 0.00033181209459040074
27.0 0.0003194446403240109
28.0 0.0003184084588259308
29.0 0.0003182506069381648
30.0 0.0003148797013444408
31.0 0.0002961487376129427
32.0 0.00027052175379974156
33.0 0.00024743766685454786
34.0 0.0002256857592625916
35.0 0.00020579998427225097
36.0 0.000189038268813506
37.0 0.00017386965729266948
38.0 0.0001613485014690905
39.0 0.0001574132034911388
40.0 0.0001490677750078641
41.0 0.00013790177558791725
42.0 0.0001282878615396144
43.0 0.00012095612436994448
44.0 0.00011214167633915717
45.0 0.00010421673782294511
46.0 9.715626623684205e-05
47.0 9.282271063116496e-05
48.0 8.696571645233427e-05
49.0 8.108410275243205e-05
50.0 7.672762907247785e-05
51.0 7.164556991989368e-05
52.0 6.712091046340094e-05
53.0 6.402983760430654e-05
54.0 6.340827259447476e-05
55.0 6.212579456204865e-05
56.0 6.0479432395632356e-05
57.0 5.871255187231619e-05
58.0 5.6732218205513816e-05
59.0 5.469844909188562e-05
60.0 5.272638831110061e-05
61.0 5.082941624023762e-05
62.0 4.9172657560503e-05
63.0 4.7723292856953955e-05
64.0 4.640794539328976e-05
65.0 4.4830504104868853e-05
66.0 4.33432435988776e-05
67.0 4.17840819038174e-05
68.0 4.0359335324500254e-05
69.0 3.890539627505912e-05
70.0 3.773843593447448e-05
71.0 3.650676651396156e-05
72.0 3.528219096983737e-05
73.0 3.440527767945646e-05
74.0 3.350747980104347e-05
75.0 3.26561659597071e-05
76.0 3.19802966664897e-05
77.0 3.1835209823474306e-05
78.0 3.183429293715699e-05
79.0 3.183429293715699e-05
80.0 3.179465449554639e-05
81.0 3.1754468203569435e-05
82.0 3.1704945367497785e-05
83.0 3.1660515386167146e-05
84.0 3.161204511239972e-05
85.0 3.160031088406889e-05
86.0 3.160031088406889e-05
87.0 3.159054611415194e-05
88.0 3.1527283185355765e-05
89.0 3.1443493604304305e-05
90.0 3.1323353389521874e-05
91.0 3.117894171029721e-05
92.0 3.0954278315859144e-05
93.0 3.057844960395481e-05
94.0 3.014447137763062e-05
95.0 2.9597164606371073e-05
96.0 2.887863910263771e-05
97.0 2.8423195872524498e-05
98.0 2.7793813070448293e-05
99.0 2.7040901735687525e-05
100.0 2.619028564470109e-05
101.0 2.5450004510283205e-05
102.0 2.4855217140189223e-05
103.0 2.403822662596923e-05
104.0 2.3244772756237742e-05
... ...
1000.0 0.000000023425324

Binning these data across irregular percentage ranges is a common way to present such distributions. You can categorize and aggregate data using pd.cut() with subsequent group_by():
import pandas as pd
import matplotlib.pyplot as plt
#sample data generation
import numpy as np
rng = np.random.default_rng(123)
n = 1000
df = pd.DataFrame({"x": range(n), "wealth_share_2017": np.sort(rng.pareto(a=100, size=n))[::-1]})
df.loc[0, "wealth_share_2017"] = 50
df["wealth_share_2017"] /= df["wealth_share_2017"].sum()
n = len(df)
#define bins in percent
#the last valueis slightly above 100% to ensure that the final bin is included
bins = [0, 0.1, 0.5, 1.0, 10.0, 50.0, 100.01]
#create figure labels for intervals from bins
labels = [f"[{start:.1f}, {stop:.1f})" for start, stop in zip(bins[:-1], bins[1:])]
#categorize data
df["cats"] = pd.cut(df["x"], bins=[n*i/100 for i in bins], include_lowest=True, right=False, labels=labels)
#and aggregate
df_plot = df.groupby(by="cats")["wealth_share_2017"].sum().mul(100)
df_plot.plot.bar(rot=45, xlabel="Income percentile", ylabel="Wealth share (%)", title=df_plot.name)
plt.tight_layout()
plt.show()

NaN in single column while importing data from URL

I am trying to import all 9 columns of the popular MPG dataset from UCI from a URL. The problem is , instead of the string values showing, Carname (the ninth column) is populated by NaN.
What is going wrong and how can one fix this? The link to the repository shows that the original dataset has 9 columns, so this should work.
From the URL and we find that the data looks like
18.0 8 307.0 130.0 3504. 12.0 70 1 "chevrolet chevelle malibu"
15.0 8 350.0 165.0 3693. 11.5 70 1 "buick skylark 320"
with unique string values on the Carname but when we import it as
import pandas as pd
# Import raw dataset from URL
url = 'http://archive.ics.uci.edu/ml/machine-learning-databases/auto-mpg/auto-mpg.data'
column_names = ['MPG', 'Cylinders', 'Displacement', 'Horsepower',
'Weight', 'Acceleration', 'Model Year', 'Origin', 'Carname']
data = pd.read_csv(url, names=column_names,
na_values='?', comment='\t',
sep=' ', skipinitialspace=True)
data.head(3)
yielding (with NaN values on Carname)
MPG Cylinders Displacement Horsepower Weight Acceleration Model Year Origin Carname
0 18.0 8 307.0 130.0 3504.0 12.0 70 1 NaN
1 15.0 8 350.0 165.0 3693.0 11.5 70 1 NaN

It’s literally in your read_csv call: comment='\t'. The only tabs are before the Carname field, which means the way you read the fle explicitely ignores that column.
You can remove the comment parameter and use the more generic separator \s+ instead to split on any whitespace (one or more spaces, a tab, etc.):
>>> pd.read_csv(url, names=column_names, na_values='?', sep='\s+')
MPG Cylinders Displacement Horsepower Weight Acceleration Model Year Origin Carname
0 18.0 8 307.0 130.0 3504.0 12.0 70 1 chevrolet chevelle malibu
1 15.0 8 350.0 165.0 3693.0 11.5 70 1 buick skylark 320
2 18.0 8 318.0 150.0 3436.0 11.0 70 1 plymouth satellite
3 16.0 8 304.0 150.0 3433.0 12.0 70 1 amc rebel sst
4 17.0 8 302.0 140.0 3449.0 10.5 70 1 ford torino
.. ... ... ... ... ... ... ... ... ...
393 27.0 4 140.0 86.0 2790.0 15.6 82 1 ford mustang gl
394 44.0 4 97.0 52.0 2130.0 24.6 82 2 vw pickup
395 32.0 4 135.0 84.0 2295.0 11.6 82 1 dodge rampage
396 28.0 4 120.0 79.0 2625.0 18.6 82 1 ford ranger
397 31.0 4 119.0 82.0 2720.0 19.4 82 1 chevy s-10
[398 rows x 9 columns]

pandas dataframe interpolate for Nans with groupby using window of discrete days of the year

The small reproducible example below sets up a dataframe that is 100 yrs in length containing some randomly generated values. It then inserts 3 100-day stretches of missing values. Using this small example, I am attempting to sort out the pandas commands that will fill in the missing days using average values for that day of the year (hence the use of .groupby) with a condition. For example, if April 12th is missing, how can the last line of code be altered such that only the 10 nearest April 12th's are used to fill in the missing value? In other words, a missing April 12th value in 1920 would be filled in using the mean April 12th values between 1915 to 1925; a missing April 12th value in 2000 would be filled in with the mean April 12th values between 1995 to 2005, etc. I tried playing around with adding a .rolling() to the lambda function in last line of script, but was unsuccessful in my attempt.
Bonus question: The example below extends from 1918 to 2018. If a value is missing on April 12th 1919, for example, it would still be nice if ten April 12ths were used to fill in the missing value even though the window couldn't be 'centered' on the missing day because of its proximity to the beginning of the time series. Is there a solution to the first question above that would be flexible enough to still use a minimum of 10 values when missing values are close to the beginning and ending of the time series?
import pandas as pd
import numpy as np
import random
# create 100 yr time series
dates = pd.date_range(start="1918-01-01", end="2018-12-31").strftime("%Y-%m-%d")
vals = [random.randrange(1, 50, 1) for i in range(len(dates))]
# Create some arbitrary gaps
vals[100:200] = vals[9962:10062] = vals[35895:35995] = [np.nan] * 100
# Create dataframe
df = pd.DataFrame(dict(
list(
zip(["Date", "vals"],
[dates, vals])
)
))
# confirm missing vals
df.iloc[95:105]
df.iloc[35890:35900]
# set a date index (for use by groupby)
df.index = pd.DatetimeIndex(df['Date'])
df['Date'] = df.index
# Need help restricting the mean to the 10 nearest same-days-of-the-year:
df['vals'] = df.groupby([df.index.month, df.index.day])['vals'].transform(lambda x: x.fillna(x.mean()))

This answers both parts
build a DF dfr that is the calculation you want
lambda function returns a dict {year:val, ...}
make sure indexes are named in reasonable way
expand out dict with apply(pd.Series)
reshape by putting year columns back into index
merge() built DF with original DF. vals column contains NaN 0 column is value to fill
finally fillna()
# create 100 yr time series
dates = pd.date_range(start="1918-01-01", end="2018-12-31")
vals = [random.randrange(1, 50, 1) for i in range(len(dates))]
# Create some arbitrary gaps
vals[100:200] = vals[9962:10062] = vals[35895:35995] = [np.nan] * 100
# Create dataframe - simplified from question...
df = pd.DataFrame({"Date":dates,"vals":vals})
df[df.isna().any(axis=1)]
ystart = df.Date.dt.year.min()
# generate rolling means for month/day. bfill for when it's start of series
dfr = (df.groupby([df.Date.dt.month, df.Date.dt.day])["vals"]
.agg(lambda s: {y+ystart:v for y,v in enumerate(s.dropna().rolling(5).mean().bfill())})
.to_frame().rename_axis(["month","day"])
)
# expand dict into columns and reshape to by indexed by month,day,year
dfr = dfr.join(dfr.vals.apply(pd.Series)).drop(columns="vals").rename_axis("year",axis=1).stack().to_frame()
# get df index back, plus vals & fillna (column 0) can be seen alongside each other
dfm = df.merge(dfr, left_on=[df.Date.dt.month,df.Date.dt.day,df.Date.dt.year], right_index=True)
# finally what we really want to do - fill tha NaNs
df.fillna(dfm[0])
analysis
taking NaN for 11-Apr-1918, default is 22 as it's backfilled from 1921
(12+2+47+47+2)/5 == 22
dfm.query("key_0==4 & key_1==11").head(7)
key_0
key_1
key_2
Date
vals
0
100
4
11
1918
1918-04-11 00:00:00
nan
22
465
4
11
1919
1919-04-11 00:00:00
12
22
831
4
11
1920
1920-04-11 00:00:00
2
22
1196
4
11
1921
1921-04-11 00:00:00
47
27
1561
4
11
1922
1922-04-11 00:00:00
47
36
1926
4
11
1923
1923-04-11 00:00:00
2
34.6
2292
4
11
1924
1924-04-11 00:00:00
37
29.4

I'm not sure how far I've gotten with the intent of your question. The approach I've taken is to satisfy two requirements
Need an arbitrary number of averages
Use those averages to fill in the NA
I have addressed the
Simply put, instead of filling in the NA with before and after dates, I fill in the NA with averages extracted from any number of years in a row.
import pandas as pd
import numpy as np
import random
# create 100 yr time series
dates = pd.date_range(start="1918-01-01", end="2018-12-31").strftime("%Y-%m-%d")
vals = [random.randrange(1, 50, 1) for i in range(len(dates))]
# Create some arbitrary gaps
vals[100:200] = vals[9962:10062] = vals[35895:35995] = [np.nan] * 100
# Create dataframe
df = pd.DataFrame(dict(
list(
zip(["Date", "vals"],
[dates, vals])
)
))
df['Date'] = pd.to_datetime(df['Date'])
df['mm-dd'] = df['Date'].apply(lambda x:'{:02}-{:02}'.format(x.month, x.day))
df['yyyy'] = df['Date'].apply(lambda x:'{:04}'.format(x.year))
df = df.iloc[:,1:].pivot(index='mm-dd', columns='yyyy')
df.columns = df.columns.droplevel(0)
df['nans'] = df.isnull().sum(axis=1)
df['10n_mean'] = df.iloc[:,:-1].sample(n=10, axis=1).mean(axis=1)
df['10n_mean'] = df['10n_mean'].round(1)
df.loc[df['nans'] >= 1]
yyyy 1918 1919 1920 1921 1922 1923 1924 1925 1926 1927 ... 2011 2012 2013 2014 2015 2016 2017 2018 nans 10n_mean
mm-dd
02-29 NaN NaN 34.0 NaN NaN NaN 2.0 NaN NaN NaN ... NaN 49.0 NaN NaN NaN 32.0 NaN NaN 76 21.6
04-11 NaN 43.0 12.0 28.0 29.0 28.0 1.0 38.0 11.0 3.0 ... 17.0 35.0 8.0 17.0 34.0 NaN 5.0 33.0 3 29.7
04-12 NaN 19.0 38.0 34.0 48.0 46.0 28.0 29.0 29.0 14.0 ... 41.0 16.0 9.0 39.0 8.0 NaN 1.0 12.0 3 21.3
04-13 NaN 33.0 26.0 47.0 21.0 26.0 20.0 16.0 11.0 7.0 ... 5.0 11.0 34.0 28.0 27.0 NaN 2.0 46.0 3 21.3
04-14 NaN 36.0 19.0 6.0 45.0 41.0 24.0 39.0 1.0 11.0 ... 30.0 47.0 45.0 14.0 48.0 NaN 16.0 8.0 3 24.7
df_mean = df.T.fillna(df['10n_mean'], downcast='infer').T
df_mean.loc[df_mean['nans'] >= 1]
yyyy 1918 1919 1920 1921 1922 1923 1924 1925 1926 1927 ... 2011 2012 2013 2014 2015 2016 2017 2018 nans 10n_mean
mm-dd
02-29 21.6 21.6 34.0 21.6 21.6 21.6 2.0 21.6 21.6 21.6 ... 21.6 49.0 21.6 21.6 21.6 32.0 21.6 21.6 76.0 21.6
04-11 29.7 43.0 12.0 28.0 29.0 28.0 1.0 38.0 11.0 3.0 ... 17.0 35.0 8.0 17.0 34.0 29.7 5.0 33.0 3.0 29.7
04-12 21.3 19.0 38.0 34.0 48.0 46.0 28.0 29.0 29.0 14.0 ... 41.0 16.0 9.0 39.0 8.0 21.3 1.0 12.0 3.0 21.3
04-13 21.3 33.0 26.0 47.0 21.0 26.0 20.0 16.0 11.0 7.0 ... 5.0 11.0 34.0 28.0 27.0 21.3 2.0 46.0 3.0 21.3
04-14 24.7 36.0 19.0 6.0 45.0 41.0 24.0 39.0 1.0 11.0 ... 30.0 47.0 45.0 14.0 48.0 24.7 16.0 8.0 3.0 24.7

Get summary data columns in new pandas dataframe from existing dataframe based on other column-ID

I'm want to summarize the data in a dataframe and add the new columns to another dataframe. My data contains appartments with an ID-number and it has surface and volume values for each room in the appartment. What I want is having a dataframe that summarizes this and gives me the total surface and volume per appartment. There are two conditions for the original dataframe:
Two conditions:
- the dataframe can contain empty cells
- when the values of surface or volume are equal for all of the rows within that ID
(so all the same values for the same ID), then the data (surface, volumes) is not
summed but one value/row is passed to the new summary column (example: 'ID 4')(as
this could be a mistake in the original dataframe and the total surface/volume was
inserted for all the rooms by the government-employee)
Initial dataframe 'data':
print(data)
ID Surface Volume
0 2 10.0 25.0
1 2 12.0 30.0
2 2 24.0 60.0
3 2 8.0 20.0
4 4 84.0 200.0
5 4 84.0 200.0
6 4 84.0 200.0
7 52 NaN NaN
8 52 96.0 240.0
9 95 8.0 20.0
10 95 6.0 15.0
11 95 12.0 30.0
12 95 30.0 75.0
13 95 12.0 30.0
Desired output from 'df':
print(df)
ID Surface Volume
0 2 54.0 135.0
1 4 84.0 200.0 #-> as the values are the same for each row of this ID in the original data, the sum is not taken, but only one of the rows is passed (see the second condition)
2 52 96.0 240.0
3 95 68.0 170.0
Tried code:
import pandas as pd
import numpy as np

df = pd.DataFrame({"ID": [2,4,52,95]})

data = pd.DataFrame({"ID": [2,2,2,2,4,4,4,52,52,95,95,95,95,95],
"Surface": [10,12,24,8,84,84,84,np.nan,96,8,6,12,30,12],
"Volume": [25,30,60,20,200,200,200,np.nan,240,20,15,30,75,30]})

print(data)

#Tried something, but no idea how to do this actually:
df["Surface"] = data.groupby("ID").agg(sum)
df["Volume"] = data.groupby("ID").agg(sum)
print(df)

Here are necessary 2 conditions - first testing if unique values per groups for each columns separately by GroupBy.transform and DataFrameGroupBy.nunique and compare by eq for equal with 1 and then second condition - it used DataFrame.duplicated by each column with ID column.
Chain both masks by & for bitwise AND and repalce matched values by NaNs by DataFrame.mask and last aggregate sum:
cols = ['Surface','Volume']
m1 = data.groupby("ID")[cols].transform('nunique').eq(1)
m2 = data[cols].apply(lambda x: x.to_frame().join(data['ID']).duplicated())
df = data[cols].mask(m1 & m2).groupby(data["ID"]).sum().reset_index()
print(df)
ID Surface Volume
0 2 54.0 135.0
1 4 84.0 200.0
2 52 96.0 240.0
3 95 68.0 170.0
If need new columns filled by aggregate sum values use GroupBy.transform :
cols = ['Surface','Volume']
m1 = data.groupby("ID")[cols].transform('nunique').eq(1)
m2 = data[cols].apply(lambda x: x.to_frame().join(data['ID']).duplicated())
data[cols] = data[cols].mask(m1 & m2).groupby(data["ID"]).transform('sum')
print(data)
ID Surface Volume
0 2 54.0 135.0
1 2 54.0 135.0
2 2 54.0 135.0
3 2 54.0 135.0
4 4 84.0 200.0
5 4 84.0 200.0
6 4 84.0 200.0
7 52 96.0 240.0
8 52 96.0 240.0
9 95 68.0 170.0
10 95 68.0 170.0
11 95 68.0 170.0
12 95 68.0 170.0
13 95 68.0 170.0

Plotting incompatible numpy arrays

I have two numpy arrays of identical length (398 rows), with the first 5 values for each as follows:
y_predicted =
[[-0.85908649]
[-1.19176482]
[-0.93658361]
[-0.83557211]
[-0.80681243]]
y_norm =
mpg
0 -0.705551
1 -1.089379
2 -0.705551
3 -0.961437
4 -0.833494
That is, the first has square brackets around each value, and the second has indexing and no square brackets.
The data is a normalised version of the first column (MPG) of the Auto-MPG dataset. The y_predicted values are results of a linear regression.
https://archive.ics.uci.edu/ml/machine-learning-databases/auto-mpg/auto-mpg.data
Would anyone know how I might convert these arrays to the same type so I can plot a scatter plot of them?
Both have shape: (398, 1)
Both have type: class 'numpy.ndarray', dtype float64
Data from the link provided
18.0 8 307.0 130.0 3504. 12.0 70 1 "chevrolet chevelle malibu"
15.0 8 350.0 165.0 3693. 11.5 70 1 "buick skylark 320"
18.0 8 318.0 150.0 3436. 11.0 70 1 "plymouth satellite"
16.0 8 304.0 150.0 3433. 12.0 70 1 "amc rebel sst"
17.0 8 302.0 140.0 3449. 10.5 70 1 "ford torino"
15.0 8 429.0 198.0 4341. 10.0 70 1 "ford galaxie 500"

The second of these looks like a pandas Series to me. If so you can do y_norm.values to get the underlying numpy array.