I want to fill values forward horizontally, but limited to one fill value only.
See the frames below: dfa has some gaps that need filling. I want the results as shown in dfb.
(Note the .T at the end of the lines, the transpose the data horizontally.)
However, dfa.fillna(0, limit=1, axis=1) fills all cells in the Name row, whereas columns 5 and 6 (i.e. the two columns to the left of 7 in the Name row) should remain NaN.
import pandas as pd
import numpy as np
dfa = pd.DataFrame({'Name':[1, np.nan, 3, np.nan, np.nan, np.nan, 7, np.nan],
'Age': [np.nan, 2, np.nan, 4, np.nan, 6, np.nan, 8]}).T
dfb = pd.DataFrame({'Name':[1, 0, 3, 0, np.nan, np.nan, 7, 0],
'Age': [np.nan, 2, 0, 4, 0, 6, 0, 8]}).T
dfc = dfa.fillna(0, limit=1, axis=1)
One idea is use forward filling for mask and then replace with DataFrame.mask chained conditions with &:
m = dfa.ffill(limit=1, axis=1).isna()
print (m)
0 1 2 3 4 5 6 7
Name False False False False True True False False
Age True False False False False False False False
dfc = dfa.mask(dfa.isna() & ~m, 0)
Or first replace all NaNs and then create NaNs by condition:
dfc = dfa.fillna(0).mask(m)
print (dfc)
0 1 2 3 4 5 6 7
Name 1.0 0.0 3.0 0.0 NaN NaN 7.0 0.0
Age NaN 2.0 0.0 4.0 0.0 6.0 0.0 8.0
Related
I have a Pandas 0.24.2 dataframe for Python 3.7x as below. I want to drop_duplicates() with the same Name based on a conditional logic. A similar question can be found here: Pandas - Conditional drop duplicates but it gets more complicated in my case
import pandas as pd
import numpy as np
df = pd.DataFrame({
'Id': [1, 2, 3, 4, 5, 6 ],
'Name': ['A', 'B', 'C', 'A', 'B', 'C' ],
'Value1':[1, np.NaN, 0, np.NaN, 1, np.NaN],
'Value2':[np.NaN, 0, np.NaN, 1, np.NaN, 0 ],
'Value3':[np.NaN, 0, np.NaN, 1, np.NaN, np.NaN]
})
How is it possible to:
Drop duplicates for same 'Name' records, keeping the one that has less NaNs?
If they have the same number of NaNs, keeping the one that has NOT a NaN in 'Value1'?
The desired output would be:
Id Name Value1 Value2 Value3
2 2 B NaN 0 0
3 3 C 0 NaN NaN
4 4 A NaN 1 1
Idea is create helper columns for both conditions, sorting and remove duplicates:
df1 = df.assign(count= df.isna().sum(axis=1),
count_val1 = df['Value1'].isna().view('i1'))
df2 = (df1.sort_values(['count', 'count_val1'])[df.columns]
.drop_duplicates('Name')
.sort_index())
print (df2)
Id Name Value1 Value2 Value3
1 2 B NaN 0.0 0.0
2 3 C 0.0 NaN NaN
3 4 A NaN 1.0 1.0
Here is a different solution. The goal is to create two columns that help sort the duplicate rows that will be deleted.
First, we create the columns.
df['count_nan'] = df.isnull().sum(axis=1)
Value1_nan = []
for row in df['Value1']:
if row >= 0:
Value1_nan.append(0)
else:
Value1_nan.append(1)
df['Value1_nan'] = Value1_nan
We then sort the columns so that the column with the most NaNs appears first.
df.sort_values(by=['Name','count_nan', 'Value1'], inplace=True, ascending = [True, True, True])
Finally, we drop the "last" duplicate line. That is, we keep the line with the smallest number of NaNs followed by the line with the smallest number of NaNs in Value1
df = df.drop_duplicates(subset = ['Name'],keep='first')
I have this dataframe presented below. I tried a solution below, but I am not sure if this is a good solution.
import pandas as pd
def creatingDataFrame():
raw_data = {'code': [1, 2, 3, 2 , 3, 3],
'Region': ['A', 'A', 'C', 'B' , 'A', 'B'],
'var-A': [2,4,6,4,6,6],
'var-B': [20, 30, 40 , 50, 10, 20],
'var-C': [3, 4 , 5, 1, 2, 3]}
df = pd.DataFrame(raw_data, columns = ['code', 'Region','var-A', 'var-B', 'var-C'])
return df
if __name__=="__main__":
df=creatingDataFrame()
df['var']=np.where(df['Region']=='A',1.0,0.0)*df['var-A']+np.where(df['Region']=='B',1.0,0.0)*df['var-B']+np.where(df['Region']=='C',1.0,0.0)*df['var-C']
I want the variable var assumes values of column 'var-A', 'var-B' or 'var-C' depending on the region provided by region 'Region'.
The result must be
df['var']
Out[50]:
0 2.0
1 4.0
2 5.0
3 50.0
4 6.0
5 20.0
Name: var, dtype: float64
You can try with lookup
df.columns=df.columns.str.split('-').str[-1]
df
Out[255]:
code Region A B C
0 1 A 2 20 3
1 2 A 4 30 4
2 3 C 6 40 5
3 2 B 4 50 1
4 3 A 6 10 2
5 3 B 6 20 3
df.lookup(df.index,df.Region)
Out[256]: array([ 2, 4, 5, 50, 6, 20], dtype=int64)
#df['var']=df.lookup(df.index,df.Region)
I have two dataframes like the following examples:
import pandas as pd
import numpy as np
df = pd.DataFrame({'a': ['20', '50', '100'], 'b': [1, np.nan, 1],
'c': [np.nan, 1, 1]})
df_id = pd.DataFrame({'b': ['50', '4954', '93920', '20'],
'c': ['123', '100', '6', np.nan]})
print(df)
a b c
0 20 1.0 NaN
1 50 NaN 1.0
2 100 1.0 1.0
print(df_id)
b c
0 50 123
1 4954 100
2 93920 6
3 20 NaN
For each identifier in df['a'], I want to null the value in df['b'] if there is no matching identifier in any row in df_id['b']. I want to do the same for column df['c'].
My desired result is as follows:
result = pd.DataFrame({'a': ['20', '50', '100'], 'b': [1, np.nan, np.nan],
'c': [np.nan, np.nan, 1]})
print(result)
a b c
0 20 1.0 NaN
1 50 NaN NaN # df_id['c'] did not contain '50'
2 100 NaN 1.0 # df_id['b'] did not contain '100'
My attempt to do this is here:
for i, letter in enumerate(['b','c']):
df[letter] = (df.apply(lambda x: x[letter] if x['a']
.isin(df_id[letter].tolist()) else np.nan, axis = 1))
The error I get:
AttributeError: ("'str' object has no attribute 'isin'", 'occurred at index 0')
This is in Python 3.5.2, Pandas version 20.1
You can solve your problem using this instead:
for letter in ['b','c']: # took off enumerate cuz i didn't need it here, maybe you do for the rest of your code
df[letter] = df.apply(lambda row: row[letter] if row['a'] in (df_id[letter].tolist()) else np.nan,axis=1)
just replace isin with in.
The problem is that when you use apply on df, x will represent df rows, so when you select x['a'] you're actually selecting one element.
However, isin is applicable for series or list-like structures which raises the error so instead we just use in to check if that element is in the list.
Hope that was helpful. If you have any questions please ask.
Adapting a hard-to-find answer from Pandas New Column Calculation Based on Existing Columns Values:
for i, letter in enumerate(['b','c']):
mask = df['a'].isin(df_id[letter])
name = letter + '_new'
# for some reason, df[letter] = df.loc[mask, letter] does not work
df.loc[mask, name] = df.loc[mask, letter]
df[letter] = df[name]
del df[name]
This isn't pretty, but seems to work.
If you have a bigger Dataframe and performance is important to you, you can first build a mask df and then apply it to your dataframe.
First create the mask:
mask = df_id.apply(lambda x: df['a'].isin(x))
b c
0 True False
1 True False
2 False True
This can be applied to the original dataframe:
df.iloc[:,1:] = df.iloc[:,1:].mask(~mask, np.nan)
a b c
0 20 1.0 NaN
1 50 NaN NaN
2 100 NaN 1.0
I have a dataframe with columns age, date and location.
I would like to count how many rows are empty across ALL columns (not some but all in the same time). I have the following code, each line works independently, but how do I say age AND date AND location isnull?
df['age'].isnull().sum()
df['date'].isnull().sum()
df['location'].isnull().sum()
I would like to return a dataframe after removing the rows with missing values in ALL these three columns, so something like the following lines but combined in one statement:
df.mask(row['location'].isnull())
df[np.isfinite(df['age'])]
df[np.isfinite(df['date'])]
You basically can use your approach, but drop the column indices:
df.isnull().sum().sum()
The first .sum() returns a per-column value, while the second .sum() will return the sum of all NaN values.
Similar to Vaishali's answer, you can use df.dropna() to drop all values that are NaN or None and only return your cleaned DataFrame.
In [45]: df = pd.DataFrame({'age': [1, 2, 3, np.NaN, 4, None], 'date': [1, 2, 3, 4, None, 5], 'location': ['a', 'b', 'c', None, 'e', 'f']})
In [46]: df
Out[46]:
age date location
0 1.0 1.0 a
1 2.0 2.0 b
2 3.0 3.0 c
3 NaN 4.0 None
4 4.0 NaN e
5 NaN 5.0 f
In [47]: df.isnull().sum().sum()
Out[47]: 4
In [48]: df.dropna()
Out[48]:
age date location
0 1.0 1.0 a
1 2.0 2.0 b
2 3.0 3.0 c
You can find the no of rows with all NaNs by
len(df) - len(df.dropna(how = 'all'))
and drop by
df = df.dropna(how = 'all')
This will drop the rows with all the NaN values
I have a pandas DataFrame in python 3.
In this DataFrame there are rows which have identical values in two columns (this can be whole sections), I'll call this a group.
Each row also has a True/False value in a column.
Now for each row I want to know if any of the rows in its group have a False value, if so, I want to assign a False value to every row in that group in another column. I've managed to do this in a for-loop, but it's quite slow:
import pandas as pd
import numpy as np
df = pd.DataFrame({'E': [np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan],
'D': [0, 1, 2, 3, 4, 5, 6],
'C': [True, True, False, False, True, True, True],
'B': ['aa', 'aa', 'aa', 'bb', 'cc', 'dd', 'dd'],
'A': [0, 0, 0, 0, 1, 1, 1]})
Which gives:
df:
A B C D E
0 0 aa True 0 NaN
1 0 aa True 1 NaN
2 0 aa False 2 NaN
3 0 bb False 3 NaN
4 1 cc True 4 NaN
5 1 dd True 5 NaN
6 1 dd True 6 NaN
Now I run the for-loop:
for i in df.index:
df.ix[i, 'E'] = df[(df['A'] == df.iloc[i]['A']) & (df['B'] == df.iloc[i]['B'])]['C'].all()
which then gives the desired result:
df:
A B C D E
0 0 aa True 0 False
1 0 aa True 1 False
2 0 aa False 2 False
3 0 bb False 3 False
4 1 cc True 4 True
5 1 dd True 5 True
6 1 dd True 6 True
When running this for my entire DataFrame of ~1 million rows this takes ages. So, looking at using .apply() to avoid a for-loop I've stumbled across the following question: apply a function to a pandas Dataframe whose retuned value is based on other rows
however:
def f(x): return False not in x
df.groupby(['A','B']).C.apply(f)
returns:
A B
0 aa False
bb True
1 cc True
dd True
Does anyone know a better way or how to fix the last case?
You could try doing a SQL-style join using pd.merge.
Perform the same groupby that you're doing, but apply min() to it to look for any cases with C == True. Then convert that to a DataFrame, rename the column as "E", and merge it back to df.
df = pd.DataFrame({'D': [0, 1, 2, 3, 4, 5, 6],
'C': [True, True, False, False, True, True, True],
'B': ['aa', 'aa', 'aa', 'bb', 'cc', 'dd', 'dd'],
'A': [0, 0, 0, 0, 1, 1, 1]})
falses = pd.DataFrame(df.groupby(['A', 'B']).C.min() == True)
falses = falses.rename(columns={'C': 'E'})
df = df.merge(falses, left_on=['A', 'B'], right_index=True)