I have the following code:
t = 12
s = numpy.array(df.Array.tolist())
s[s<t] = 0
thresh = numpy.where(s>0, s-t, 0)
df['NewArray'] = list(thresh)
while it works, surely there must be a more pandas-like way of doing it.
EDIT:
df.Array.head() looks like this:
0 [0.771511552006, 0.771515476223, 0.77143569165...
1 [3.66720695274, 3.66722560562, 3.66684636758, ...
2 [2.3047433839, 2.30475510675, 2.30451676559, 2...
3 [0.999991522708, 0.999996609066, 0.99989319662...
4 [1.11132718786, 1.11133284052, 0.999679589875,...
Name: Array, dtype: object
IIUC you can simply subtract and use clip_lower:
In [29]: df["NewArray"] = (df["Array"] - 12).clip_lower(0)
In [30]: df
Out[30]:
Array NewArray
0 10 0
1 11 0
2 12 0
3 13 1
4 14 2
Related
I have data like below:
df = pd.DataFrame()
df["collection_amount"] = 100, 200, 300
df["25%_coll"] = 1, 0, 1
df["75%_coll"] = 0, 1, 1
df["month"] = 4, 5, 6
I want to create a output like below:
basically if 25% is 1 then it should create a column based on month as a new column.
Please help me thank you.
This should work: do ask if something doesn't make sense
for i in range(len(df)):
if df['25%_coll'][i]==1:
df['month_%i_25%%_coll'%df.month[i]]=[df.collection_amount[i] if k==i else 0 for k in range(len(df))]
if df['75%_coll'][i]==1:
df['month_%i_75%%_coll'%df.month[i]]=[df.collection_amount[i] if k==i else 0 for k in range(len(df))]
To build the new columns you could try the following:
df2 = df.melt(id_vars=["month", "collection_amount"])
df2.loc[df2["value"].eq(0), "collection_amount"] = 0
df2["new_cols"] = "month_" + df2["month"].astype("str") + "_" + df2["variable"]
df2 = df2.pivot_table(
index="month", columns="new_cols", values="collection_amount",
fill_value=0, aggfunc="sum"
).reset_index(drop=True)
.melt() the dataframe with index columns month and collection_amount.
Set the appropriate collection_amount values to 0.
Build the new column names in column new_cols.
month collection_amount variable value new_cols
0 4 100 25%_coll 1 month_4_25%_coll
1 5 0 25%_coll 0 month_5_25%_coll
2 6 300 25%_coll 1 month_6_25%_coll
3 4 0 75%_coll 0 month_4_75%_coll
4 5 200 75%_coll 1 month_5_75%_coll
5 6 300 75%_coll 1 month_6_75%_coll
Use .pivot_table() on this dataframe to build the new columns.
The rest isn't completely clear: Either use df = pd.concat([df, df2], axis=1), or df.merge(df2, ...) to merge on month (with .reset_index() without drop=True).
Result for the sample dataframe
df = pd.DataFrame({
"collection_amount": [100, 200, 300],
"25%_coll": [1, 0, 1], "75%_coll": [0, 1, 1],
"month": [4, 5, 6]
})
is
new_cols month_4_25%_coll month_4_75%_coll month_5_25%_coll \
0 100 0 0
1 0 0 0
2 0 0 0
new_cols month_5_75%_coll month_6_25%_coll month_6_75%_coll
0 0 0 0
1 200 0 0
2 0 300 300
Can use please help with below problem:
Given two dataframes df1 and df2, need to get something like result dataframe.
import pandas as pd
import numpy as np
feature_list = [ str(i) for i in range(6)]
df1 = pd.DataFrame( {'value' : [0,3,0,4,2,5]})
df2 = pd.DataFrame(0, index=np.arange(6), columns=feature_list)
Expected Dataframe :
Need to be driven by comparing values from df1 with column names (features) in df2. if they match, we put 1 in resultDf
Here's expected output (or resultsDf):
I think you need:
(pd.get_dummies(df1['value'])
.rename(columns = str)
.reindex(columns = df2.columns,
index = df2.index,
fill_value = 0))
0 1 2 3 4 5
0 1 0 0 0 0 0
1 0 0 0 1 0 0
2 1 0 0 0 0 0
3 0 0 0 0 1 0
4 0 0 1 0 0 0
5 0 0 0 0 0 1
I want to create a row number series - but not override my date index.
I can do it with a loop but I think there must be an easier way?
_cnt = [ ]
for i in range ( len ( df ) ):
_cnt.append ( i )
df[ 'row' ] = _cnt
Thanks.
Probably the easiest way:
df['row'] = range(len(df))
>>> df
0 1
0 0.444965 0.993382
1 0.001578 0.174628
2 0.663239 0.072992
3 0.664612 0.291361
4 0.486449 0.528354
>>> df['row'] = range(len(df))
>>> df
0 1 row
0 0.444965 0.993382 0
1 0.001578 0.174628 1
2 0.663239 0.072992 2
3 0.664612 0.291361 3
4 0.486449 0.528354 4
Given a pandas dataframe like this:
df = pd.DataFrame({'col1': [1, 2, 3], 'col2': [4, 5, 6]})
col1 col2
0 1 4
1 2 5
2 3 6
I would like to do something equivalent to this using a function but without passing "by value" or as a global variable the whole dataframe (it could be huge and then it would give me a memory error):
i = -1
for index, row in df.iterrows():
if i < 0:
i = index
continue
c1 = df.loc[i][0] + df.loc[index][0]
c2 = df.loc[i][1] + df.loc[index][1]
df.ix[index, 0] = c1
df.ix[index, 1] = c2
i = index
col1 col2
0 1 4
1 3 9
2 6 15
i.e., I would like to have a function which will give me the previous output:
def my_function(two_rows):
row1 = two_rows[0]
row2 = two_rows[1]
c1 = row1[0] + row2[0]
c2 = row1[1] + row2[1]
row2[0] = c1
row2[1] = c2
return row2
df.apply(my_function, axis=1)
df
col1 col2
0 1 4
1 3 9
2 6 15
Is there a way of doing this?
What you've demonstrated is a cumsum
df.cumsum()
col1 col2
0 1 4
1 3 9
2 6 15
def f(df):
n = len(df)
r = range(1, n)
for j in df.columns:
for i in r:
df[j].values[i] += df[j].values[i - 1]
return df
f(df)
To define a function as a loop that does this in place
Slow cell by cell
def f(df):
n = len(df)
r = range(1, n)
for j in df.columns:
for i in r:
df[j].values[i] += df[j].values[i - 1]
return df
f(df)
col1 col2
0 1 4
1 3 9
2 6 15
Compromise between memory and efficiency
def f(df):
for j in df.columns:
df[j].values[:] = df[j].values.cumsum()
return df
f(df)
f(df)
col1 col2
0 1 4
1 3 9
2 6 15
Note that you don't need to return df. I chose to for convenience.
when i have tried with below code iam getting unexpected results.
This is my code:
def copy_blanks(df, column):
df.iloc[:, -1] = df.iloc[:, column]
df.loc[df.iloc[:, column] == '', -1] = ''
My input file:
word,number
abc,0
adf,0
gfsgs,0
,0
sdfgsd,0
fgsdfg,0
sfdgs,0
Iam getting like below output:
word,number,word_clean
NA,0,abc
NA,0,adf
NA,0,gfsgs
,0,
NA,0,sdfgsd
NA,0,fgsdfg
NA,0,sfdgs
I Want to get like below output:
word,number,word_clean
abc,0,abc
adf,0,adf
gfsgs,0,gfsgs
,0,
sdfgsd,0,sdfgsd
fgsdfg,0,fgsdfg
sfdgs,0,sfdgs
PLease suggest me.I think the issue is getting by this code [df.loc[df.iloc[:, column] == '', -1] = ''].
IIUC you need fillna for replacing NaN to empty string:
print (df)
word number
0 abc 0
1 adf 0
2 gfsgs 0
3 NaN 0
4 sdfgsd 0
5 fgsdfg 0
6 sfdgs 0
def copy_blanks(df, column):
df['new'] = df.iloc[:, column]
df['new'] = df['new'].fillna('')
return df
column = 0
print (copy_blanks(df, column))
word number new
0 abc 0 abc
1 adf 0 adf
2 gfsgs 0 gfsgs
3 NaN 0
4 sdfgsd 0 sdfgsd
5 fgsdfg 0 fgsdfg
6 sfdgs 0 sfdgs