I am working on olympics dataset and want to create another dataframe that has total number of athletes and total number of medals won by type for each country.
Using following pivot_table gives me an error "ValueError: Grouper for 'ID' not 1-dimensional"
pd.pivot_table(olymp, index='NOC', columns=['ID','Medal'], values=['ID','Medal'], aggfunc={'ID':pd.Series.nunique,'Medal':'count'}).sort_values(by='Medal')
Result should have one row for each country with columns for totalAthletes, gold, silver, bronze. Not sure how to go about it using pivot_table. I can do this using merge of crosstab but would like to use just one pivottable statement.
Here is what original df looks like.
Update
I would like to get the medal breakdown as well e.g. gold, silver, bronze. Also I need unique count of athlete id's so I use nunique since one athlete may participate in multiple events. Same with medal, ignoring NA values
IIUC:
out = df.pivot_table('ID', 'NOC', 'Medal', aggfunc='count', fill_value=0)
out['ID'] = df[df['Medal'].notna()].groupby('NOC')['ID'].nunique()
Output:
>>> out
Medal Bronze Gold Silver ID
NOC
AFG 2 0 0 1
AHO 0 0 1 1
ALG 8 5 4 14
ANZ 5 20 4 25
ARG 91 91 92 231
.. ... ... ... ...
VIE 0 1 3 3
WIF 5 0 0 4
YUG 93 130 167 317
ZAM 1 0 1 2
ZIM 1 17 4 16
[149 rows x 4 columns]
Old answer
You can't have the same column for columns and values:
out = olymp.pivot_table(index='NOC', values=['ID','Medal'],
aggfunc={'ID':pd.Series.nunique, 'Medal':'count'}) \
.sort_values('Medal', ascending=False)
print(out)
# Output
ID Medal
NOC
USA 9653 5637
URS 2948 2503
GER 4872 2165
GBR 6281 2068
FRA 6170 1777
.. ... ...
GAM 33 0
GBS 15 0
GEQ 26 0
PNG 61 0
LBA 68 0
[230 rows x 2 columns]
Another way to get the result above:
out = olym.groupby('NOC').agg({'ID': pd.Series.nunique, 'Medal': 'count'}) \
.sort_values('Medal', ascending=False)
print(out)
# Output
ID Medal
NOC
USA 9653 5637
URS 2948 2503
GER 4872 2165
GBR 6281 2068
FRA 6170 1777
.. ... ...
GAM 33 0
GBS 15 0
GEQ 26 0
PNG 61 0
LBA 68 0
[230 rows x 2 columns]
Related
I have created a set of 4 clusters using kmeans, but I'd like to reorder the clusters in an ascending manner to have a predictable way of outputting an analysis every time the script is executed.
The resulting df with the clusters is something like:
customer_id recency frequency monetary_value recency_cluster \
0 44792907512250289 21 1 43.76 0
1 4277896431638207047 443 1 73.13 1
2 1509512561185834874 559 1 37.50 1
3 -8259919882769629944 437 1 34.38 1
4 8269311313560571571 133 2 324.78 0
5 6521698907264712834 311 1 6.32 3
6 9102795320443090762 340 1 174.99 3
7 6203217338400763719 39 1 77.50 0
8 7633758030510673403 625 1 95.26 2
9 -2417721548925747504 644 1 76.84 2
frequency_cluster monetary_value_cluster
0 1 0
1 1 0
2 1 0
3 1 0
4 0 1
5 1 0
6 1 1
7 1 0
8 1 0
9 1 0
The recency clusters are not sorted by the data, I'd like for example that the recency cluster 0 to be the one with the min value = 1.0 (recency cluster 1).
recency_cluster count mean std min 25% 50% 75% max
0 17609.0 700.900960 56.895995 609.0 651.0 697.0 749.0 807.0
1 16458.0 102.692672 62.952229 1.0 47.0 101.0 159.0 210.0
2 17166.0 515.971746 56.592490 418.0 466.0 517.0 567.0 608.0
3 18634.0 317.599227 58.852980 211.0 269.0 319.0 367.0 416.0
Using something like:
rfm_df.groupby('recency_cluster')['recency'].transform('min')
Will return a colum with the min value of each clusters
0 1
1 418
2 418
3 418
4 1
...
69862 609
69863 1
69864 211
69865 609
69866 211
I guess there's got to be a way to convert this categories [1,211,418,609] into [0, 1, 2, 3] in order to get the desired result but I can't come up with a solution.
Or maybe there's a better approach to the problem.
Edit: I did this and I think it's working:
rfm_df['recency_normalized_cluster'] = rfm_df.groupby('recency_cluster')['recency'].transform('min').astype('category').cat.codes
rfm_df['recency_normalized_cluster'] = rfm_df.groupby('recency_cluster')['recency'].transform('min').astype('category').cat.codes
I have Dataframe:
teamId pts xpts
Liverpool 82 59
Man City 57 63
Leicester 53 47
Chelsea 48 55
And I'm trying to add new columns that identify the team position by each column
I wanna get this:
teamId pts xpts №pts №xpts
Liverpool 82 59 1 2
Man City 57 63 2 1
Leicester 53 47 3 4
Chelsea 48 53 4 3
I tried to do something similar with the following code, but to no avail. The result is a list
df = [df.sort_values(by=i, ascending=False).assign(new_col=lambda x: range(1, len(df) + 1)) for i in df.columns]
You can use np.argsort:
df[["no_pts", "no_xpts"]] = df.apply(lambda x: np.argsort(-x)) + 1
We pass each column but negated so that it gives the ascending order sort's indices. Since it starts from 0, we add 1 at the end.
to get
teamId pts xpts no_pts no_xpts
Liverpool 82 59 1 2
Man City 57 63 2 1
Leicester 53 47 3 4
Chelsea 48 55 4 3
Solutions if teamId is index:
Use DataFrame.rank with all columns, add prefix and append to original DataFrame:
df = df.join(df.rank(method='dense', ascending=False).astype(int).add_prefix('no_'))
print (df)
pts xpts no_pts no_xpts
teamId
Liverpool 82 59 1 2
Man City 57 63 2 1
Leicester 53 47 3 4
Chelsea 48 55 4 3
If need specify columns for processing use list:
cols = ['pts','xpts']
df = df.join(df[cols].rank(method='dense', ascending=False).astype(int).add_prefix('no_'))
Consider I have the following dataframe:
Survived Pclass Sex Age Fare
0 0 3 male 22.0 7.2500
1 1 1 female 38.0 71.2833
2 1 3 female 26.0 7.9250
3 1 1 female 35.0 53.1000
4 0 3 male 35.0 8.0500
I used the get_dummies() function to create dummy variable. The code and output are as follows:
one_hot = pd.get_dummies(dataset, columns = ['Category'])
This will return:
Survived Pclass Age Fare Sex_female Sex_male
0 0 3 22 7.2500 0 1
1 1 1 38 71.2833 1 0
2 1 3 26 7.9250 1 0
3 1 1 35 53.1000 1 0
4 0 3 35 8.0500 0 1
What I would like to have is a single column for Sex having the values 0 or 1 instead of 2 columns.
Interestingly, when I used get_dummies() on a different dataframe, it worked just like I wanted.
For the following dataframe:
Category Message
0 ham Go until jurong point, crazy.. Available only ...
1 ham Ok lar... Joking wif u oni...
2 spam Free entry in 2 a wkly comp to win FA Cup final...
3 ham U dun say so early hor... U c already then say...
4 ham Nah I don't think he goes to usf, he lives aro...
With the code:
one_hot = pd.get_dummies(dataset, columns = ['Category'])
It returns:
Message ... Category_spam
0 Go until jurong point, crazy.. Available only ... ... 0
1 Ok lar... Joking wif u oni... ... 0
2 Free entry in 2 a wkly comp to win FA Cup fina... ... 1
3 U dun say so early hor... U c already then say... ... 0
4 Nah I don't think he goes to usf, he lives aro... ... 0
Why does get_dummies() work differently on these two dataframes?
How can I make sure I get the 2nd output everytime?
Here are multiple ways you can do:
from sklearn.preprocessing import LabelEncoder
lbl=LabelEncoder()
df['Sex_encoded'] = lbl.fit_transform(df['Sex'])
# using only pandas
df['Sex_encoded'] = df['Sex'].map({'male': 0, 'female': 1})
Survived Pclass Sex Age Fare Sex_encoded
0 0 3 male 22.0 7.2500 0
1 1 1 female 38.0 71.2833 1
2 1 3 female 26.0 7.9250 1
3 1 1 female 35.0 53.1000 1
4 0 3 male 35.0 8.0500 0
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
i have a pandas dataframe
id no_of_rows
1 2689
2 1515
3 3826
4 814
5 1650
6 2292
7 1867
8 2096
9 1618
10 923
11 766
12 191
i want to divide id's into 5 different bins based on their no. of rows,
such that every bin has approx(equal no of rows)
and assign it as a new column bin
One approach i thought was
df.no_of_rows.sum() = 20247
div_factor = 20247//5 == 4049
if we add 1st and 2nd row its sum = 2689+1515 = 4204 > div_factor.
Therefore assign bin = 1 where id = 1.
Now look for the next ones
id no_of_rows bin
1 2689 1
2 1515 2
3 3826 3
4 814 4
5 1650 4
6 2292 5
7 1867
8 2096
9 1618
10 923
11 766
12 191
But this method proved wrong.
Is there a way to have 5 bins such that every bin has good amount of stores(approximately equal)
You can use an approach based on percentiles.
n_bins = 5
dfa = df.sort_values(by='no_of_rows').cumsum()
df['bin'] = dfa.no_of_rows.apply(lambda x: int(n_bins*x/dfa.no_of_rows.max()))
And then you can check with
df.groupby('bin').sum()
The more records you have the more fair it will be in terms of dispersion.