I have a dataframe with currently 22 rows
index value
0 23
1 22
2 19
...
21 20
to this dataframe, i want to add 72 rows to make the dataframe exactly 100 rows. So i need to fill loc[22:99] but with a certain value, let's say 100.
I tried something like this
uncon_dstn_2021['balance'].loc[22:99] = 100
but did not work. Any idea?
You can do reindex
out = df.reindex(df.index.tolist() + list(range(22, 99+1)), fill_value = 100)
You can also use pd.concat:
df1 = pd.concat([df, pd.DataFrame({'balance': [100]*(100-len(df))})], ignore_index=True)
print(df1)
# Output
balance
0 1
1 14
2 11
3 11
4 10
.. ...
96 100
97 100
98 100
99 100
[100 rows x 1 columns]
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
A value in 'Target_Labels' is either 0.0,1.0,2.0 in float64.
Based on this value, I would like to look up a value in one of three columns 'B365A','B365D','B365H' and multiply this value by 10 in a new column. This operation needs to be row wise throughout the entire DataFrame.
I have tried many combinations but nothing seem to work...
final['amount'] = final['Target_Labels'].apply((lambda x: 'B365A' * 10 if x==0.0 else ('B365D' * 10 if x ==1 else 'B365H' * 10))
def prod(x, var1, var2, var3, var4):
if (x[var4])==0:
x[var3]*10
elif (x[var4])==1:
x[var1]*10
else:
x[var2]*10
return x
final['montant'] = final.apply(lambda x: prod(x, 'B365D', 'B365H','B365A', 'Target_Labels'), axis=1)
I'm new to Pandas and any help is welcome...
Use numpy to indexing and get individual cells:
array = final.values
row = range(len(df))
col = final['Target_Labels'] - 1
>>> final
B365A B365D B365H Target_Labels
0 11 12 13 1
1 11 12 13 2
2 11 12 13 3
>>> final['amount'] = final.values[(range(len(final)),
final['Target_Labels'] - 1)] * 10
>>> final
B365A B365D B365H Target_Labels amount
0 11 12 13 1 110
1 11 12 13 2 120
2 11 12 13 3 130
I have a column in pandas dataset of random values ranging btw 100 and 500.
I need to create a new column 'deciles' out of it - like ranking, total of 20 deciles. I need to assign rank number out of 20 based on the value.
10 to 20 - is the first decile, number 1
20 to 30 - is the second decile, number 2
x = np.random.randint(100,501,size=(1000)) # column of 1000 rows with values ranging btw 100, 500.
df['credit_score'] = x
df['credit_decile_rank'] = df['credit_score'].map( lambda x: int(x/20) )
df.head()
Use integer division by 10:
df = pd.DataFrame({
'credit_score':[4,15,24,55,77,81],
})
df['credit_decile_rank'] = df['credit_score'] // 10
print (df)
credit_score credit_decile_rank
0 4 0
1 15 1
2 24 2
3 55 5
4 77 7
5 81 8
I have a dataframe and want to use pct_chg method to calculate the % change between only 2 of the selected columns, B and C, and put the output into a new column. the below code doesnt seem to work. can anyone help me?
df2 = pd.DataFrame(np.random.randint(0,50,size=(100, 4)), columns=list('ABCD'))
df2['new'] = df2.pct_change(axis=1)['B']['C']
Try:
df2['new'] = df2[['B','C']].pct_change(axis=1)['C']
pct_change returns pct_change across all the columns, you can select the required column and assign to a new variable.
df2['new'] = df2.pct_change(axis=1)['C']
A B C D new
0 29 4 29 5 6.250000
1 14 35 2 40 -0.942857
2 5 18 31 10 0.722222
3 17 10 42 41 3.200000
4 24 48 47 35 -0.020833
IIUC, you can just do the following:
df2['new'] = (df2['C']-df2['B'])/df2['B']
I would like to apply a custom function to each level within a multiindex.
For example, I have the dataframe
df = pd.DataFrame(np.arange(16).reshape((4,4)),
columns=pd.MultiIndex.from_product([['OP','PK'],['PRICE','QTY']]))
of which I want to add a column for each level 0 column, called "Value" which is the result of the following function;
def my_func(df, scale):
return df['QTY']*df['PRICE']*scale
where the user supplies the "scale" value.
Even in setting up this example, I am not sure how to show the result I want. But I know I want the final dataframe's multiindex column to be
pd.DataFrame(columns=pd.MultiIndex.from_product([['OP','PK'],['PRICE','QTY','Value']]))
Even if that wasn't had enough, I want to apply one "scale" value for the "OP" level 0 column and a different "scale" value to the "PK" column.
Use:
def my_func(df, scale):
#select second level of columns
df1 = df.xs('QTY', axis=1, level=1).values *df.xs('PRICE', axis=1, level=1) * scale
#create MultiIndex in columns
df1.columns = pd.MultiIndex.from_product([df1.columns, ['val']])
#join to original
return pd.concat([df, df1], axis=1).sort_index(axis=1)
print (my_func(df, 10))
OP PK
PRICE QTY val PRICE QTY val
0 0 1 0 2 3 60
1 4 5 200 6 7 420
2 8 9 720 10 11 1100
3 12 13 1560 14 15 2100
EDIT:
For multiple by scaled values different for each level is possible use list of values:
print (my_func(df, [10, 20]))
OP PK
PRICE QTY val PRICE QTY val
0 0 1 0 2 3 120
1 4 5 200 6 7 840
2 8 9 720 10 11 2200
3 12 13 1560 14 15 4200
Use groupby + agg, and then concatenate the pieces together with pd.concat.
scale = 10
v = df.groupby(level=0, axis=1).agg(lambda x: x.values.prod(1) * scale)
v.columns = pd.MultiIndex.from_product([v.columns, ['value']])
pd.concat([df, v], axis=1).sort_index(axis=1, level=0)
OP PK
PRICE QTY value PRICE QTY value
0 0 1 0 2 3 60
1 4 5 200 6 7 420
2 8 9 720 10 11 1100
3 12 13 1560 14 15 2100