I have the following series I obtained from a read_html:
series:
1 417.951
2 621.710
3 164.042
4 189.963
5 555.123
6 213.494
7 2.873.093
I would like to remove the . in order to apply some function to the numbers in that column.
So the desired output would be:
series:
1 417951
2 621710
3 164042
4 189963
5 555123
6 213494
7 2873093
I have tried a replace recieving the same result:
df.replace('.','')
and turn the series to a dataframe to see if that was the problem but it keeps returning initial series.
You need assign output to Series and if necessary convert to int, but also is necessary escape . by \ and add parameter regex in Series.replace:
series = series.replace('\.','', regex=True)
print (series)
1 417951
2 621710
3 164042
4 189963
5 555123
6 213494
7 2873093
Name: a, dtype: object
series = series.replace('\.','', regex=True).astype(int)
print (series)
1 417951
2 621710
3 164042
4 189963
5 555123
6 213494
7 2873093
Name: a, dtype: int32
Another solution is use str.replace:
series = series.str.replace('.','')
print (series)
1 417951
2 621710
3 164042
4 189963
5 555123
6 213494
7 2873093
Name: a, dtype: object
But beter is use thousands parameter in read_html:
df = pd.read_html(url, thousands='.')
Related
I am trying to count common string values in sequential rows of a panda series using a user defined function and to write an output into a new column. I figured out individual steps, but when I put them together, I get a wrong result. Could you please tell me the best way to do this? I am a very beginner Pythonista!
My pandas df is:
df = pd.DataFrame({"Code": ['d7e', '8e0d', 'ft1', '176', 'trk', 'tr71']})
My string comparison loop is:
x='d7e'
y='8e0d'
s=0
for i in y:
b=str(i)
if b not in x:
s+=0
else:
s+=1
print(s)
the right result for these particular strings is 2
Note, when I do def func(x,y): something happens to s counter and it doesn't produce the right result. I think I need to reset it to 0 every time the loop runs.
Then, I use df.shift to specify the position of y and x in a series:
x = df["Code"]
y = df["Code"].shift(periods=-1, axis=0)
And finally, I use df.apply() method to run the function:
df["R1SB"] = df.apply(func, axis=0)
and I get None values in my new column "R1SB"
My correct output would be:
"Code" "R1SB"
0 d7e None
1 8e0d 2
2 ft1 0
3 176 1
4 trk 0
5 tr71 2
Thank you for your help!
TRY:
df['R1SB'] = df.assign(temp=df.Code.shift(1)).apply(
lambda x: np.NAN
if pd.isna(x['temp'])
else sum(i in str(x['temp']) for i in str(x['Code'])),
1,
)
OUTPUT:
Code R1SB
0 d7e NaN
1 8e0d 2.0
2 ft1 0.0
3 176 1.0
4 trk 0.0
5 tr71 2.0
I have a pandas series as follows...
0 2039-03-16
1 2056-01-21
2 2051-11-18
3 2064-03-05
4 2048-06-05
Name: BIRTH, dtype: datetime64
It was created from string data as follows
s = data['BIRTH']
s = pd.to_datetime(s)
s
I want to convert all dates after year 2040 to 1940
I can do this for a single record as follows
s.iloc[0].replace(year=d.year-100)
but I really want to just run it over the whole series. I can't work it out. Help!??
PS - I know there's ways outside of pandas using Python's DT module but I'd like to learn how to do this within Pandas please
Using DateOffset is the obvious choice here:
df['date'] - pd.offsets.DateOffset(years=100)
0 1939-03-16
1 1956-01-21
2 1951-11-18
3 1964-03-05
4 1948-06-05
Name: date, dtype: datetime64[ns]
Assign it back:
df['date'] -= pd.offsets.DateOffset(years=100)
df
date
0 1939-03-16
1 1956-01-21
2 1951-11-18
3 1964-03-05
4 1948-06-05
We have the offsets module to deal with non-fixed frequencies, it comes in handy in situations like these.
To fix your code, you'd have wanted to apply datetime.replace rowwise using apply (not recommended):
df['date'].apply(lambda x: x.replace(year=x.year-100))
0 1939-03-16
1 1956-01-21
2 1951-11-18
3 1964-03-05
4 1948-06-05
Name: date, dtype: datetime64[ns]
Or using a list comprehension,
df.assign(date=[x.replace(year=x.year-100) for x in df['date']])
date
0 1939-03-16
1 1956-01-21
2 1951-11-18
3 1964-03-05
4 1948-06-05
Neither of these handle NaT entries very well.
I have a dataframe that looks like this. It will be used to map values using two categorical variables. Maybe converting this to a dictionary would be better.
The 2nd data-frame is very large with a screenshot shown below. I want to take the values from the categorical variables to create a new attribute (column) based on the 1st data-frame.
For example...
A row with FICO_cat of (700,720] and OrigLTV_cat of (75,80] would receive a value of 5.
A row with FICO_cat of (700,720] and OrigLTV_cat of (85,90] would receive a value of 6.
Is there an efficient way to do this?
If your column labels are the FICO_cat values, and your Index is OrigLTV_cat, this should work:
Given a dataframe df:
780+ (740,780) (720,740)
(60,70) 3 3 3
(70,75) 4 5 4
(75,80) 3 1 2
Do:
df = df.unstack().reset_index()
df.rename(columns = {'level_0' : 'FICOCat', 'level_1' : 'OrigLTV', 0 : 'value'}, inplace = True)
Output:
FICOCat OrigLTV value
0 780+ (60,70) 3
1 780+ (70,75) 4
2 780+ (75,80) 3
3 (740,780) (60,70) 3
4 (740,780) (70,75) 5
5 (740,780) (75,80) 1
6 (720,740) (60,70) 3
7 (720,740) (70,75) 4
8 (720,740) (75,80) 2
need some help/advise how to wrangling dates into a Pandas DataFrame. I have Python list looking like this:
['',
'20180715:1700-20180716:1600',
'20180716:1700-20180717:1600',
'20180717:1700-20180718:1600',
'20180718:1700-20180719:1600',
'20180719:1700-20180720:1600',
'20180721:CLOSED',
'20180722:1700-20180723:1600',
'20180723:1700-20180724:1600',
'20180724:1700-20180725:1600',
'20180725:1700-20180726:1600',
'20180726:1700-20180727:1600',
'20180728:CLOSED']
Is there an easy way to transform this into a Pandas DataFrame with two columns (start time and end time)?
Sample:
L = ['',
'20180715:1700-20180716:1600',
'20180716:1700-20180717:1600',
'20180717:1700-20180718:1600',
'20180718:1700-20180719:1600',
'20180719:1700-20180720:1600',
'20180721:CLOSED',
'20180722:1700-20180723:1600',
'20180723:1700-20180724:1600',
'20180724:1700-20180725:1600',
'20180725:1700-20180726:1600',
'20180726:1700-20180727:1600',
'20180728:CLOSED']
I think best here is use list comprehension with split by separator and filter out values with no splitter:
df = pd.DataFrame([x.split('-') for x in L if '-' in x], columns=['start','end'])
print (df)
start end
0 20180715:1700 20180716:1600
1 20180716:1700 20180717:1600
2 20180717:1700 20180718:1600
3 20180718:1700 20180719:1600
4 20180719:1700 20180720:1600
5 20180722:1700 20180723:1600
6 20180723:1700 20180724:1600
7 20180724:1700 20180725:1600
8 20180725:1700 20180726:1600
9 20180726:1700 20180727:1600
Pandas solution is also possible, especially if need process Series - here is used split and dropna:
s = pd.Series(L)
df = s.str.split('-', expand=True).dropna(subset=[1])
df.columns = ['start','end']
print (df)
start end
1 20180715:1700 20180716:1600
2 20180716:1700 20180717:1600
3 20180717:1700 20180718:1600
4 20180718:1700 20180719:1600
5 20180719:1700 20180720:1600
7 20180722:1700 20180723:1600
8 20180723:1700 20180724:1600
9 20180724:1700 20180725:1600
10 20180725:1700 20180726:1600
11 20180726:1700 20180727:1600
Suppose I have a set of measurements that were obtained by varying two parameters, knob_b and knob_2 (in practice there are a lot more):
data = np.empty((6,3), dtype=np.float)
data[:,0] = [3,4,5,3,4,5]
data[:,1] = [1,1,1,2,2,2]
data[:,2] = np.random.random(6)
df = pd.DataFrame(data, columns=['knob_1', 'knob_2', 'signal'])
i.e., df is
knob_1 knob_2 signal
0 3 1 0.076571
1 4 1 0.488965
2 5 1 0.506059
3 3 2 0.415414
4 4 2 0.771212
5 5 2 0.502188
Now, considering each parameter on its own, I want to find the minimum value that was measured for each setting of this parameter (ignoring the settings of all other parameters). The pedestrian way of doing this is:
new_index = []
new_data = []
for param in df.columns:
if param == 'signal':
continue
group = df.groupby(param)['signal'].min()
for (k,v) in group.items():
new_index.append((param, k))
new_data.append(v)
new_index = pd.MultiIndex.from_tuples(new_index,
names=('parameter', 'value'))
df2 = pd.Series(index=new_index, data=new_data)
resulting df2 being:
parameter value
knob_1 3 0.495674
4 0.277030
5 0.398806
knob_2 1 0.485933
2 0.277030
dtype: float64
Is there a better way to do this, in particular to get rid of the inner loop?
It seems to me that the result of the df.groupby operation already has everything I need - if only there was a way to somehow create a MultiIndex from it without going through the list of tuples.
Use the keys argument of pd.concat():
pd.concat([df.groupby('knob_1')['signal'].min(),
df.groupby('knob_2')['signal'].min()],
keys=['knob_1', 'knob_2'],
names=['parameter', 'value'])