I am a geologist needing to clean up data.
I have a .csv file containing drilling intervals, that I imported as a pandas dataframe that looks like this:
hole_name from to interval_type
0 A 0 1 Gold
1 A 1 2 Gold
2 A 2 4 Inferred_fault
3 A 4 6 NaN
4 A 6 7 NaN
5 A 7 8 NaN
6 A 8 9 Inferred_fault
7 A 9 10 NaN
8 A 10 11 Inferred_fault
9 B2 11 12 Inferred_fault
10 B2 12 13 Inferred_fault
11 B2 13 14 NaN
For each individual "hole_name", I would like to group/merge the "from" and "to" range for consecutive intervals associated with the same "interval_type". The NaN values can be dropped, they are of no use to me (but I already know how to do this, so it is fine).
Based on the example above, I would like to get something like this:
hole_name from to interval_type
0 A 0 2 Gold
2 A 2 4 Inferred_fault
3 A 4 8 NaN
6 A 8 9 Inferred_fault
7 A 9 10 NaN
8 A 10 11 Inferred_fault
9 B2 11 13 Inferred_fault
11 B2 13 14 NaN
I have looked around and tried to use groupby or pyranges but cannot figure how to do this...
Thanks a lot in advance for your help!
This should do the trick:
import pandas as pd
import numpy as np
from itertools import groupby
# create dataframe
data = {
'hole_name': ['A', 'A', 'A', 'A', 'A', 'A', 'A', 'A', 'A', 'B', 'B', 'B'],
'from': [0, 1, 2, 4, 6, 7, 8, 9, 10, 11, 12, 13],
'to': [1, 2, 4, 6, 7, 8, 9, 10, 11, 12, 13, 14],
'interval_type': ['Gold', 'Gold', 'Inferred_fault', np.nan, np.nan, np.nan,
'Inferred_fault', np.nan, 'Inferred_fault', 'Inferred_fault',
'Inferred_fault', np.nan]
}
df = pd.DataFrame(data=data)
# create auxiliar column that groups repetitive consecutive values
grouped = [list(g) for k, g in groupby(list(zip(df.hole_name.tolist(), df.interval_type.tolist())))]
df['interval_type_id'] = np.repeat(range(len(grouped)),[len(x) for x in grouped])+1
# aggregate results
cols = df.columns[:-1]
vals = []
for idx, group in df.groupby(['interval_type_id', 'hole_name']):
vals.append([group['hole_name'].iloc[0], group['from'].min(), group['to'].max(), group['interval_type'].iloc[0]])
result = pd.DataFrame(data=vals, columns=cols)
result
result should be:
hole_name from to interval_type
A 0 2 Gold
A 2 4 Inferred_fault
A 4 8
A 8 9 Inferred_fault
A 9 10
A 10 11 Inferred_fault
B 11 13 Inferred_fault
B 13 14
EDIT: added hole_name to the groupby function.
You can first build an indicator column for grouping. Then use agg to merge the sub groups to get from and to.
(
df.assign(ind=df.interval_type.fillna(''))
.assign(ind=lambda x: x.ind.ne(x.ind.shift(1).bfill()).cumsum())
.groupby(['hole_name', 'ind'])
.agg({'from':'first', 'to':'last', 'interval_type': 'first'})
.reset_index()
.drop('ind',1)
)
hole_name from to interval_type
0 A 0 2 Gold
1 A 2 4 Inferred_fault
2 A 4 8 NaN
3 A 8 9 Inferred_fault
4 A 9 10 NaN
5 A 10 11 Inferred_fault
6 B 11 13 Inferred_fault
7 B 13 14 NaN
Related
I want to reshape the data so that the values in the index column become the columns
My Data frame:
Gender_Male Gender_Female Location_london Location_North Location_South
Cat
V 5 4 4 2 3
W 15 12 12 7 8
X 11 15 16 4 6
Y 22 18 21 9 9
Z 8 7 7 4 4
Desired Data frame:
Is there an easy way to do this? I also have 9 other categorical variables in my data set in addition to the Gender and Location variables. I have only included two variables to keep the example simple.
Code to create the example dataframe:
df1 = pd.DataFrame({
'Cat' : ['V','W', 'X', 'Y', 'Z'],
'Gender_Male' : [5, 15, 11, 22, 8],
'Gender_Female' : [4, 12, 15, 18, 7],
'Location_london': [4,12, 16, 21, 7],
'Location_North' : [2, 7, 4, 9, 4],
'Location_South' : [3, 8, 6, 9, 4]
}).set_index('Cat')
df1
You can transpose the dataframe and then split and set the new index:
Transpose
dft = df1.T
print(dft)
Cat V W X Y Z
Gender_Male 5 15 11 22 8
Gender_Female 4 12 15 18 7
Location_london 4 12 16 21 7
Location_North 2 7 4 9 4
Location_South 3 8 6 9 4
Split and set the new index
dft.index = dft.index.str.split('_', expand=True)
dft.columns.name = None
print(dft)
V W X Y Z
Gender Male 5 15 11 22 8
Female 4 12 15 18 7
Location london 4 12 16 21 7
North 2 7 4 9 4
South 3 8 6 9 4
I have two pandas DataFrames, both containing the same categories but different 'id' columns. In order to illustrate, the first table looks like this:
df = pd.DataFrame({
'id': list(np.arange(1, 12)),
'category': ['a', 'a', 'a', 'a', 'b', 'b', 'c', 'c', 'c', 'c', 'c'],
'weight': list(np.random.randint(1, 5, 11))
})
df['weight_sum'] = df.groupby('category')['weight'].transform('sum')
df['p'] = df['weight'] / df['weight_sum']
Output:
id category weight weight_sum p
0 1 a 4 14 0.285714
1 2 a 4 14 0.285714
2 3 a 2 14 0.142857
3 4 a 4 14 0.285714
4 5 b 4 8 0.500000
5 6 b 4 8 0.500000
6 7 c 3 15 0.200000
7 8 c 4 15 0.266667
8 9 c 2 15 0.133333
9 10 c 4 15 0.266667
10 11 c 2 15 0.133333
The second contains only 'id' and 'category'.
What I'm trying to do is to create a third DataFrame, that would have inherit the id of the second DataFrame, plus three new columns for the ids of the first DataFrame - each should be selected based on the p column, which represents its weight within that category.
I've tried multiple solutions and was thinking of applying np.random.choice and .apply(), but couldn't figure out a way to make that work.
EDIT:
The desired output would be something like:
user_id id_1 id_2 id_3
0 2 3 1 2
1 3 2 2 3
2 4 1 3 1
With each id being selected based on the its probability and respective category (both DataFrames have this column), and the same not showing up more than once for the same user_id.
Desired DataFrame
IIUC, you want to select random IDs of the same category with weighted probabilities. For this you can construct a helper dataframe (dfg) and use apply:
df2 = pd.DataFrame({
'id': np.random.randint(1, 12, size=11),
'category': ['a', 'a', 'a', 'a', 'b', 'b', 'c', 'c', 'c', 'c', 'c']})
dfg = df.groupby('category').agg(list)
df3 = df2.join(df2['category']
.apply(lambda r: pd.Series(np.random.choice(dfg.loc[r, 'id'],
p=dfg.loc[r, 'p'],
size=3)))
.add_prefix('id_')
)
Output:
id category id_0 id_1 id_2
0 11 a 2 3 3
1 10 a 2 3 1
2 4 a 1 2 3
3 7 a 2 1 4
4 5 b 6 5 5
5 10 b 6 5 6
6 8 c 9 8 8
7 11 c 7 8 7
8 11 c 10 8 8
9 4 c 9 10 10
10 1 c 11 11 9
This question already has answers here:
Pandas: how to merge two dataframes on a column by keeping the information of the first one?
(4 answers)
Closed 1 year ago.
I have two dataframes, something like this:
df1 = pd.DataFrame({
'Identity': [3, 4, 5, 6, 7, 8, 9],
'Value': [1, 2, 3, 4, 5, 6, 7],
'Notes': ['a', 'b', 'c', 'd', 'e', 'f', 'g'],
})
df2 = pd.DataFrame({
'Identity': [4, 8],
'Value': [0, 128],
})
In[3]: df1
Out[3]:
Identity Value Notes
0 3 1 a
1 4 2 b
2 5 3 c
3 6 4 d
4 7 5 e
5 8 6 f
6 9 7 g
In[4]: df2
Out[4]:
Identity Value
0 4 0
1 8 128
I'd like to use df2 to overwrite df1 but only where values exist in df2, so I end up with:
Identity Value Notes
0 3 1 a
1 4 0 b
2 5 3 c
3 6 4 d
4 7 5 e
5 8 128 f
6 9 7 g
I've been searching through all the various merge, combine, join functions etc, but I can't seem to find one that does what I want. Is there a simple way of doing this?
Use:
df1['Value'] = df1['Identity'].map(df2.set_index('Identity')['Value']).fillna(df1['Value'])
Or try reset_index with reindex and set_index with fillna:
df1['Value'] = df2.set_index('Identity').reindex(df1['Identity'])
.reset_index(drop=True)['Value'].fillna(df1['Value'])
>>> df1
Identity Value Notes
0 3 1.0 a
1 4 0.0 b
2 5 3.0 c
3 6 4.0 d
4 7 5.0 e
5 8 128.0 f
6 9 7.0 g
>>>
This fills missing rows in df2 with NaN and fills the NaNs with df1 values.
I have the following sample DataFrame
import pandas as pd
import numpy as np
df = pd.DataFrame({'Tag': ['A', 'A', 'A', 'B', 'B', 'B', 'B', 'C', 'C', 'C', 'C', 'C', 'C'],
'ID': [11, 12, 16, 19, 14, 9, 4, 13, 6, 18, 21, 1, 2],
'Value': [1, 13, 11, 12, 2, 3, 4, 5, 6, 7, 8, 9, 10]})
to which I add the percentage of the Value using
df['Percent_value'] = df['Value'].rank(method='dense', pct=True)
and add the Order using pd.cut() with pre-defined percentage bins
percentage = np.array([10, 20, 50, 70, 100])/100
df['Order'] = pd.cut(df['Percent_value'], bins=np.insert(percentage, 0, 0), labels = [1,2,3,4,5])
which gives
Tag ID Value Percent_value Order
0 A 11 1 0.076923 1
1 A 12 13 1.000000 5
2 A 16 11 0.846154 5
3 B 19 12 0.923077 5
4 B 14 2 0.153846 2
5 B 9 3 0.230769 3
6 B 4 4 0.307692 3
7 C 13 5 0.384615 3
8 C 6 6 0.461538 3
9 C 18 7 0.538462 4
10 C 21 8 0.615385 4
11 C 1 9 0.692308 4
12 C 2 10 0.769231 5
My Question
Now, instead of having a single percentage array (bins) for all Tags (groups), I have a separate percentage array for each Tag group. i.e., A, B and C. How can I apply df.groupby('Tag') and then apply pd.cut() using different percentage bins for each group from the following dictionary? Is there some direct-way avoiding for loops as I do below?
percentages = {'A': np.array([10, 20, 50, 70, 100])/100,
'B': np.array([20, 40, 60, 90, 100])/100,
'C': np.array([30, 50, 60, 80, 100])/100}
Desired outcome (Note: Order is now computed for each Tag using different bins):
Tag ID Value Percent_value Order
0 A 11 1 0.076923 1
1 A 12 13 1.000000 5
2 A 16 11 0.846154 5
3 B 19 12 0.923077 5
4 B 14 2 0.153846 1
5 B 9 3 0.230769 2
6 B 4 4 0.307692 2
7 C 13 5 0.384615 2
8 C 6 6 0.461538 2
9 C 18 7 0.538462 3
10 C 21 8 0.615385 4
11 C 1 9 0.692308 4
12 C 2 10 0.769231 4
My Attempt
orders = []
for k, g in df.groupby(['Tag']):
percentage = percentages[k]
g['Order'] = pd.cut(g['Percent_value'], bins=np.insert(percentage, 0, 0), labels = [1,2,3,4,5])
orders.append(g)
df_final = pd.concat(orders, axis=0, join='outer')
You can apply pd.cut within groupby,
df['Order'] = df.groupby('Tag').apply(lambda x: pd.cut(x['Percent_value'], bins=np.insert(percentages[x.name],0,0), labels=[1,2,3,4,5])).reset_index(drop = True)
Tag ID Value Percent_value Order
0 A 11 1 0.076923 1
1 A 12 13 1.000000 5
2 A 16 11 0.846154 5
3 B 19 12 0.923077 5
4 B 14 2 0.153846 1
5 B 9 3 0.230769 2
6 B 4 4 0.307692 2
7 C 13 5 0.384615 2
8 C 6 6 0.461538 2
9 C 18 7 0.538462 3
10 C 21 8 0.615385 4
11 C 1 9 0.692308 4
12 C 2 10 0.769231 4
Is there an opposite function of pandas.DataFrame.droplevel where I can keep some levels of the multi-level index/columns using either the level name or index?
Example:
df = pd.DataFrame([
[1, 2, 3, 4],
[5, 6, 7, 8],
[9, 10, 11, 12],
[13, 14, 15, 16]
], columns=['a','b','c','d']).set_index(['a','b','c']).T
a 1 5 9 13
b 2 6 10 14
c 3 7 11 15
d 4 8 12 16
Both the following commands can return the following dataframe:
df.droplevel(['a','b'], axis=1)
df.droplevel([0, 1], axis=1)
c 3 7 11 15
d 4 8 12 16
I am looking for a "keeplevel" command such that both the following commands can return the following dataframe:
df.keeplevel(['a','b'], axis=1)
df.keeplevel([0, 1], axis=1)
a 1 5 9 13
b 2 6 10 14
d 4 8 12 16
There is no keeplevel because it would be redundant: in a closed and well-defined set, when you define what you want to drop, you automatically define what you want to keep
You may get the difference from what you have and what droplevel returns.
def keeplevel(df, levels, axis=1):
return df.droplevel(df.axes[axis].droplevel(levels).names, axis=axis)
>>> keeplevel(df, [0, 1])
a 1 5 9 13
b 2 6 10 14
d 4 8 12 16
Using set to find the different
df.droplevel(list(set(df.columns.names)-set(['a','b'])),axis=1)
Out[134]:
a 1 5 9 13
b 2 6 10 14
d 4 8 12 16
You can modify the Index objects, which should be fast. Note, this will even modify inplace.
def keep_level(df, keep, axis):
idx = pd.MultiIndex.from_arrays([df.axes[axis].get_level_values(x) for x in keep])
df.set_axis(idx, axis=axis, inplace=True)
return df
keep_level(df.copy(), ['a', 'b'], 1) # Copy to not modify original for illustration
#a 1 5 9 13
#b 2 6 10 14
#d 4 8 12 16
keep_level(df.copy(), [0, 1], 1)
#a 1 5 9 13
#b 2 6 10 14
#d 4 8 12 16