My dataframe looks like this:
Customer ID
Age
Is True
123
31
1
124
33
1
125
45
0
126
27
0
127
37
1
128
39
0
129
49
0
130
30
0
131
30
0
132
38
1
I can create age histogram like this:
df.Age.hist()
plt.title('Age')
plt.xlabel('Age')
plt.ylabel('Frequency')
And I will get:
I would like to add a legend of the 'Is True' field. For each Bin, I would like to see what portion is 1. How can I do that?
I'm not sure you can do that with Matplotlib. But I know you can with Plotly.
import plotly.express as px
df = px.data.tips()
fig = px.histogram(df, x="total_bill", color="sex")
fig.show()
more here:
https://plotly.com/python/histograms/
Related
So I have these two dataframes and I would like to get a new dataframe which consists of the kronecker product of the rows of the two dataframes. What is the correct way to this?
As an example:
DataFrame1
c1 c2
0 10 100
1 11 110
2 12 120
and
DataFrame2
a1 a2
0 5 7
1 1 10
2 2 4
Then I would like to have the following matrix:
c1a1 c1a2 c2a1 c2a2
0 50 70 500 700
1 11 110 110 1100
2 24 48 240 480
I hope my question is clear.
PS. I saw this question was posted here kronecker product pandas dataframes. However, the answer given is not the correct answer (I believe to mine and the original question, but definitely not to mine). The answer there gives a Kronecker product of both dataframes, but I only want it over the rows.
Create MultiIndex by MultiIndex.from_product, convert both columns to MultiIndex by DataFrame.reindex and multiple Dataframe, last flatten MultiIndex:
c = pd.MultiIndex.from_product([df1, df2])
df = df1.reindex(c, axis=1, level=0).mul(df2.reindex(c, axis=1, level=1))
df.columns = df.columns.map(lambda x: f'{x[0]}{x[1]}')
print (df)
c1a1 c1a2 c2a1 c2a2
0 50 70 500 700
1 11 110 110 1100
2 24 48 240 480
Use numpy for efficiency:
import numpy as np
pd.DataFrame(np.einsum('nk,nl->nkl', df1, df2).reshape(df1.shape[0], -1),
columns=pd.MultiIndex.from_product([df1, df2]).map(''.join)
)
Output:
c1a1 c1a2 c2a1 c2a2
0 50 70 500 700
1 11 110 110 1100
2 24 48 240 480
Hi I've tried to find similar, answered questions before sending this.
Im importing a csv into Jupyter, and it seems to import as a single column, with 800 rows and 1 column.
import pandas as pd
df = pd.read_csv ('pokemon_data.csv')
df.head()
where as the same method on another file, works exactly as expected.
Using this data that was in the comments:
#,Name,Type 1,Type 2,HP,Attack,Defense,Sp. Atk,Sp. Def,Speed,Generation,Legendary
1,Bulbasaur,Grass,Poison,45,49,49,65,65,45,1,FALSE
2,Ivysaur,Grass,Poison,60,62,63,80,80,60,1,FALSE
3,Venusaur,Grass,Poison,80,82,83,100,100,80,1,FALSE
3,Mega Venusaur,Grass,Poison,80,100,123,122,120,80,1,FALSE
4,Charmander,Fire,,39,52,43,60,50,65,1,FALSE
5,Charmeleon,Fire,,58,64,58,80,65,80,1,FALSE
And using
import pandas as pd
df = pd.read_csv('pokemon.csv',sep=',')
print(df.head())
Yields the following results
# Name Type 1 Type 2 HP Attack Defense Sp. Atk Sp. Def Speed Generation Legendary
0 1 Bulbasaur Grass Poison 45 49 49 65 65 45 1 FALSE
1 2 Ivysaur Grass Poison 60 62 63 80 80 60 1 FALSE
2 3 Venusaur Grass Poison 80 82 83 100 100 80 1 FALSE
3 3 Mega Venusaur Grass Poison 80 100 123 122 120 80 1 FALSE
4 4 Charmander Fire NaN 39 52 43 60 50 65 1 FALSE
I'm analyzing a data-frame and want to check more detailed lists
but even though I searched some solutions from google,
I don't understand why the result is not changed.
what is the problem??
import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt
import numpy as np
# Import data
df = df = pd.read_csv(r"C:\Users\Administrator\Desktop\medical.txt")
pd.set_option("display.max_rows", 50)
pd.set_option('display.max_columns', 15)
print(df)
id age gender height weight ap_hi ap_lo cholesterol gluc
0 0 18393 2 168 62.0 110 80 1 1
1 1 20228 1 156 85.0 140 90 3 1
2 2 18857 1 165 64.0 130 70 3 1
3 3 17623 2 169 82.0 150 100 1 1
4 4 17474 1 156 56.0 100 60 1 1
... ... ... ... ... ... ... ... ...
69995 99993 19240 2 168 76.0 120 80 1 1
69996 99995 22601 1 158 126.0 140 90 2 2
69997 99996 19066 2 183 105.0 180 90 3 1
69998 99998 22431 1 163 72.0 135 80 1 2
69999 99999 20540 1 170 72.0 120 80 2 1
Look at https://pandas.pydata.org/pandas-docs/stable/user_guide/options.html at "Frequently used options" chapter.
You can see that if the "max_rows" is lower than the total number of rows in your dataframe then it is displayed like your results.
Below a copy past of the interesting part in the link that I gave you:
if there are a way to display enough columns
pd.set_option('display.width',1000)
or
pd.set_option('display.width',None)
but to rows may be you only use
df.head(50)
or
df.tail(50)
or follows to DisplayAll
pd.set_option("display.max_rows", None)
Why set that is useless:
The second parameter is not the maximum number of rows that can be viewed, but an internal template parameter
code as follows:
set_option = CallableDynamicDoc(_set_option, _set_option_tmpl)
CallableDynamicDoc：
class CallableDynamicDoc:
def __init__(self, func, doc_tmpl):
self.__doc_tmpl__ = doc_tmpl
self.__func__ = func
def __call__(self, *args, **kwds):
return self.__func__(*args, **kwds)
#property
def __doc__(self):
opts_desc = _describe_option("all", _print_desc=False)
opts_list = pp_options_list(list(_registered_options.keys()))
return self.__doc_tmpl__.format(opts_desc=opts_desc, opts_list=opts_list)
I've researched previous similar questions, but couldn't find any applicable leads:
I have a dataframe, called "df" which is roughly structured as follows:
Income Income_Quantile Score_1 Score_2 Score_3
0 100000 5 75 75 100
1 97500 5 80 76 94
2 80000 5 79 99 83
3 79000 5 88 78 91
4 70000 4 55 77 80
5 66348 4 65 63 57
6 67931 4 60 65 57
7 69232 4 65 59 62
8 67948 4 64 64 60
9 50000 3 66 50 60
10 49593 3 58 51 50
11 49588 3 58 54 50
12 48995 3 59 59 60
13 35000 2 61 50 53
14 30000 2 66 35 77
15 12000 1 22 60 30
16 10000 1 15 45 12
Using the "Income_Quantile" column and the following "for-loop", I divided the dataframe into a list of 5 subset dataframes (which each contain observations from the same income quantile):
dfs = []
for level in df.Income_Quantile.unique():
df_temp = df.loc[df.Income_Quantile == level]
dfs.append(df_temp)
Now, I would like to apply the following function for calculating the spearman correlation, p-value and t-statistic to the dataframe (fyi: scipy.stats functions are used in the main function):
def create_list_of_scores(df):
df_result = pd.DataFrame(columns=cols)
df_result.loc['t-statistic'] = [ttest_ind(df['Income'], df[x])[0] for x in cols]
df_result.loc['p-value'] = [ttest_ind(df['Income'], df[x])[1] for x in cols]
df_result.loc['correlation'] = [spearmanr(df['Income'], df[x])[1] for x in cols]
return df_result
The functions that "create_list_of_scores" uses, i.e. "ttest_ind" and "ttest_ind", can be accessed from scipy.stats as follows:
from scipy.stats import ttest_ind
from scipy.stats import spearmanr
I tested the function on one subset of the dataframe:
data = dfs[1]
result = create_list_of_scores(data)
It works as expected.
However, when it comes to applying the function to the entire list of dataframes, "dfs", a lot of issues arise. If I apply it to the list of dataframes as follows:
result = pd.concat([create_list_of_scores(d) for d in dfs], axis=1)
I get the output as the columns "Score_1, Score_2, and Score_3" x 5.
I would like to:
Have just three columns "Score_1, Score_2, and Score_3".
Index the output using the t-statistic, p-value and correlations as the first level index, and; the "Income_Quantile" as the second level index.
Here is what I have in mind:
Score_1 Score_2 Score_3
t-statistic 1
2
3
4
5
p-value 1
2
3
4
5
correlation 1
2
3
4
5
Any idea on how I can merge the output of my function as requested?
I think better is use GroupBy.apply:
cols = ['Score_1','Score_2','Score_3']
def create_list_of_scores(df):
df_result = pd.DataFrame(columns=cols)
df_result.loc['t-statistic'] = [ttest_ind(df['Income'], df[x])[0] for x in cols]
df_result.loc['p-value'] = [ttest_ind(df['Income'], df[x])[1] for x in cols]
df_result.loc['correlation'] = [spearmanr(df['Income'], df[x])[1] for x in cols]
return df_result
df = df.groupby('Income_Quantile').apply(create_list_of_scores).swaplevel(0,1).sort_index()
print (df)
Score_1 Score_2 Score_3
Income_Quantile
correlation 1 NaN NaN NaN
2 NaN NaN NaN
3 6.837722e-01 0.000000e+00 1.000000e+00
4 4.337662e-01 6.238377e-01 4.818230e-03
5 2.000000e-01 2.000000e-01 2.000000e-01
p-value 1 8.190692e-03 8.241377e-03 8.194933e-03
2 5.887943e-03 5.880440e-03 5.888611e-03
3 3.606128e-13 3.603267e-13 3.604996e-13
4 5.584822e-14 5.587619e-14 5.586583e-14
5 3.861801e-06 3.862192e-06 3.864736e-06
t-statistic 1 1.098143e+01 1.094719e+01 1.097856e+01
2 1.297459e+01 1.298294e+01 1.297385e+01
3 2.391611e+02 2.391927e+02 2.391736e+02
4 1.090548e+02 1.090479e+02 1.090505e+02
5 1.594605e+01 1.594577e+01 1.594399e+01
import numpy as np
xlist = np.arange(1, 100).tolist()
df = pd.DataFrame(xlist,columns=['Numbers'],dtype=int)
pd.cut(df['Numbers'],5)
how to assign column name to each distinct intervals created ?
IIUC, you can use pd.concat function and join them in a new data frame based on indexes:
# get indexes
l = df.index.tolist()
n =20
indexes = [l[i:i + n] for i in range(0, len(l), n)]
# create new data frame
new_df = pd.concat([df.iloc[x].reset_index(drop=True) for x in indexes], axis=1)
new_df.columns = ['Numbers'+str(x) for x in range(new_df.shape[1])]
print(new_df)
Numbers0 Numbers1 Numbers2 Numbers3 Numbers4
0 1 21 41 61 81.0
1 2 22 42 62 82.0
2 3 23 43 63 83.0
3 4 24 44 64 84.0
4 5 25 45 65 85.0