I can't get a hold of what is going wrong here.
d = {'x' : [1,4,6,9],
'y' : [1,4,6,8]}
df = pd.DataFrame(d)
#filter columns based on value in specific row
df_VIP = df.iloc[:,df.iloc[1:2,:]<3]
I get the error. An this also happens with my real dataframe...
ValueError: Buffer has wrong number of dimensions (expected 1, got 2)
I hope anyone has a hint to solve it. Thanks!
If possible, select by one row, e.g. second by 1 with convert to numpy array, because used iloc:
d = {'x' : [1,4,6,9],
'y' : [1,2,6,8]}
df = pd.DataFrame(d)
df_VIP = df.iloc[:,df.iloc[1,:].to_numpy()<3]
print (df_VIP)
y
0 1
1 2
2 6
3 8
If use your select - one row DataFrame instead Series flatten values by indexing:
print (df.iloc[1:2,:])
x y
1 4 2
print (df.iloc[1:2,:].to_numpy())
[[4 2]]
print (df.iloc[1:2,:].to_numpy()[0])
[4 2]
df_VIP = df.iloc[:,df.iloc[1:2,:].to_numpy()[0]<3]
Good Afternoon!
I have a pandas dataframe with an index and a count.
dictionary = {1:5,2:10,4:3,5:2}
df = pd.DataFrame.from_dict(dictionary , orient = 'index' , columns = ['count'])
What I want to do is check from df.index.min() to df.index.max() that the index increment is 1. If a value is missing like in my case the 3 is missing then I want to add 3 to the index with a 0 in the count.
The output will look like the below df2 but done in a programmatic fashion so I can use it on a much bigger dataframe.
RESULTS EXAMPLE DF:
dictionary2 = {1:5,2:10,3:0,4:3,5:2}
df2 = pd.DataFrame.from_dict(dictionary2 , orient = 'index' , columns = ['count'])
Thank you much!!!
Ensure the index is sorted:
df = df.sort_index()
Create an array that starts from the minimum index to the maximum index
complete_array = np.arange(df.index.min(), df.index.max() + 1)
Reindex, fill the null value with 0, and optionally change the dtype to Pandas Int:
df.reindex(complete_array, fill_value=0).astype("Int16")
count
1 5
2 10
3 0
4 3
5 2
I have a pandas dataframe with about 100 million rows. I am interested in deduplicating it but have some criteria that I haven't been able to find documentation for.
I would like to deduplicate the dataframe, ignoring one column that will differ. If that row is a duplicate, except for that column, I would like to only keep the row that has a specific string, say X.
Sample dataframe:
import pandas as pd
df = pd.DataFrame(columns = ["A","B","C"],
data = [[1,2,"00X"],
[1,3,"010"],
[1,2,"002"]])
Desired output:
>>> df_dedup
A B C
0 1 2 00X
1 1 3 010
So, alternatively stated, the row index 2 would be removed because row index 0 has the information in columns A and B, and X in column C
As this data is slightly large, I hope to avoid iterating over rows, if possible. Ignore Index is the closest thing I've found to the built-in drop_duplicates().
If there is no X in column C then the row should require that C is identical to be deduplicated.
In the case in which there are matching A and B in a row, but have multiple versions of having an X in C, the following would be expected.
df = pd.DataFrame(columns=["A","B","C"],
data = [[1,2,"0X0"],
[1,2,"X00"],
[1,2,"0X0"]])
Output should be:
>>> df_dedup
A B C
0 1 2 0X0
1 1 2 X00
Use DataFrame.duplicated on columns A and B to create a boolean mask m1 corresponding to condition where values in column A and B are not duplicated, then use Series.str.contains + Series.duplicated on column C to create a boolean mask corresponding to condition where C contains string X and C is not duplicated. Finally using these masks filter the rows in df.
m1 = ~df[['A', 'B']].duplicated()
m2 = df['C'].str.contains('X') & ~df['C'].duplicated()
df = df[m1 | m2]
Result:
#1
A B C
0 1 2 00X
1 1 3 010
#2
A B C
0 1 2 0X0
1 1 2 X00
Does the column "C" always have X as the last character of each value? You could try creating a column D with 1 if column C has an X or 0 if it does not. Then just sort the values using sort_values and finally use drop_duplicates with keep='last'
import pandas as pd
df = pd.DataFrame(columns = ["A","B","C"],
data = [[1,2,"00X"],
[1,3,"010"],
[1,2,"002"]])
df['D'] = 0
df.loc[df['C'].str[-1] == 'X', 'D'] = 1
df.sort_values(by=['D'], inplace=True)
df.drop_duplicates(subset=['A', 'B'], keep='last', inplace=True)
This is assuming you also want to drop duplicates in case there is no X in the 'C' column among the duplicates of columns A and B
Here is another approach. I left 'count' (a helper column) in for transparency.
# use df as defined above
# count the A,B pairs
df['count'] = df.groupby(['A', 'B']).transform('count').squeeze()
m1 = (df['count'] == 1)
m2 = (df['count'] > 1) & df['C'].str.contains('X') # could be .endswith('X')
print(df.loc[m1 | m2]) # apply masks m1, m2
A B C count
0 1 2 00X 2
1 1 3 010 1
I load some machine learning data from a CSV file. The first 2 columns are observations and the remaining columns are features.
Currently, I do the following:
data = pandas.read_csv('mydata.csv')
which gives something like:
data = pandas.DataFrame(np.random.rand(10,5), columns = list('abcde'))
I'd like to slice this dataframe in two dataframes: one containing the columns a and b and one containing the columns c, d and e.
It is not possible to write something like
observations = data[:'c']
features = data['c':]
I'm not sure what the best method is. Do I need a pd.Panel?
By the way, I find dataframe indexing pretty inconsistent: data['a'] is permitted, but data[0] is not. On the other side, data['a':] is not permitted but data[0:] is.
Is there a practical reason for this? This is really confusing if columns are indexed by Int, given that data[0] != data[0:1]
2017 Answer - pandas 0.20: .ix is deprecated. Use .loc
See the deprecation in the docs
.loc uses label based indexing to select both rows and columns. The labels being the values of the index or the columns. Slicing with .loc includes the last element.
Let's assume we have a DataFrame with the following columns:
foo, bar, quz, ant, cat, sat, dat.
# selects all rows and all columns beginning at 'foo' up to and including 'sat'
df.loc[:, 'foo':'sat']
# foo bar quz ant cat sat
.loc accepts the same slice notation that Python lists do for both row and columns. Slice notation being start:stop:step
# slice from 'foo' to 'cat' by every 2nd column
df.loc[:, 'foo':'cat':2]
# foo quz cat
# slice from the beginning to 'bar'
df.loc[:, :'bar']
# foo bar
# slice from 'quz' to the end by 3
df.loc[:, 'quz'::3]
# quz sat
# attempt from 'sat' to 'bar'
df.loc[:, 'sat':'bar']
# no columns returned
# slice from 'sat' to 'bar'
df.loc[:, 'sat':'bar':-1]
sat cat ant quz bar
# slice notation is syntatic sugar for the slice function
# slice from 'quz' to the end by 2 with slice function
df.loc[:, slice('quz',None, 2)]
# quz cat dat
# select specific columns with a list
# select columns foo, bar and dat
df.loc[:, ['foo','bar','dat']]
# foo bar dat
You can slice by rows and columns. For instance, if you have 5 rows with labels v, w, x, y, z
# slice from 'w' to 'y' and 'foo' to 'ant' by 3
df.loc['w':'y', 'foo':'ant':3]
# foo ant
# w
# x
# y
Note: .ix has been deprecated since Pandas v0.20. You should instead use .loc or .iloc, as appropriate.
The DataFrame.ix index is what you want to be accessing. It's a little confusing (I agree that Pandas indexing is perplexing at times!), but the following seems to do what you want:
>>> df = DataFrame(np.random.rand(4,5), columns = list('abcde'))
>>> df.ix[:,'b':]
b c d e
0 0.418762 0.042369 0.869203 0.972314
1 0.991058 0.510228 0.594784 0.534366
2 0.407472 0.259811 0.396664 0.894202
3 0.726168 0.139531 0.324932 0.906575
where .ix[row slice, column slice] is what is being interpreted. More on Pandas indexing here: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-advanced
Lets use the titanic dataset from the seaborn package as an example
# Load dataset (pip install seaborn)
>> import seaborn.apionly as sns
>> titanic = sns.load_dataset('titanic')
using the column names
>> titanic.loc[:,['sex','age','fare']]
using the column indices
>> titanic.iloc[:,[2,3,6]]
using ix (Older than Pandas <.20 version)
>> titanic.ix[:,[‘sex’,’age’,’fare’]]
or
>> titanic.ix[:,[2,3,6]]
using the reindex method
>> titanic.reindex(columns=['sex','age','fare'])
Also, Given a DataFrame
data
as in your example, if you would like to extract column a and d only (e.i. the 1st and the 4th column), iloc mothod from the pandas dataframe is what you need and could be used very effectively. All you need to know is the index of the columns you would like to extract. For example:
>>> data.iloc[:,[0,3]]
will give you
a d
0 0.883283 0.100975
1 0.614313 0.221731
2 0.438963 0.224361
3 0.466078 0.703347
4 0.955285 0.114033
5 0.268443 0.416996
6 0.613241 0.327548
7 0.370784 0.359159
8 0.692708 0.659410
9 0.806624 0.875476
You can slice along the columns of a DataFrame by referring to the names of each column in a list, like so:
data = pandas.DataFrame(np.random.rand(10,5), columns = list('abcde'))
data_ab = data[list('ab')]
data_cde = data[list('cde')]
And if you came here looking for slicing two ranges of columns and combining them together (like me) you can do something like
op = df[list(df.columns[0:899]) + list(df.columns[3593:])]
print op
This will create a new dataframe with first 900 columns and (all) columns > 3593 (assuming you have some 4000 columns in your data set).
Here's how you could use different methods to do selective column slicing, including selective label based, index based and the selective ranges based column slicing.
In [37]: import pandas as pd
In [38]: import numpy as np
In [43]: df = pd.DataFrame(np.random.rand(4,7), columns = list('abcdefg'))
In [44]: df
Out[44]:
a b c d e f g
0 0.409038 0.745497 0.890767 0.945890 0.014655 0.458070 0.786633
1 0.570642 0.181552 0.794599 0.036340 0.907011 0.655237 0.735268
2 0.568440 0.501638 0.186635 0.441445 0.703312 0.187447 0.604305
3 0.679125 0.642817 0.697628 0.391686 0.698381 0.936899 0.101806
In [45]: df.loc[:, ["a", "b", "c"]] ## label based selective column slicing
Out[45]:
a b c
0 0.409038 0.745497 0.890767
1 0.570642 0.181552 0.794599
2 0.568440 0.501638 0.186635
3 0.679125 0.642817 0.697628
In [46]: df.loc[:, "a":"c"] ## label based column ranges slicing
Out[46]:
a b c
0 0.409038 0.745497 0.890767
1 0.570642 0.181552 0.794599
2 0.568440 0.501638 0.186635
3 0.679125 0.642817 0.697628
In [47]: df.iloc[:, 0:3] ## index based column ranges slicing
Out[47]:
a b c
0 0.409038 0.745497 0.890767
1 0.570642 0.181552 0.794599
2 0.568440 0.501638 0.186635
3 0.679125 0.642817 0.697628
### with 2 different column ranges, index based slicing:
In [49]: df[df.columns[0:1].tolist() + df.columns[1:3].tolist()]
Out[49]:
a b c
0 0.409038 0.745497 0.890767
1 0.570642 0.181552 0.794599
2 0.568440 0.501638 0.186635
3 0.679125 0.642817 0.697628
Another way to get a subset of columns from your DataFrame, assuming you want all the rows, would be to do:
data[['a','b']] and data[['c','d','e']]
If you want to use numerical column indexes you can do:
data[data.columns[:2]] and data[data.columns[2:]]
Its equivalent
>>> print(df2.loc[140:160,['Relevance','Title']])
>>> print(df2.ix[140:160,[3,7]])
if Data frame look like that:
group name count
fruit apple 90
fruit banana 150
fruit orange 130
vegetable broccoli 80
vegetable kale 70
vegetable lettuce 125
and OUTPUT could be like
group name count
0 fruit apple 90
1 fruit banana 150
2 fruit orange 130
if you use logical operator np.logical_not
df[np.logical_not(df['group'] == 'vegetable')]
more about
https://docs.scipy.org/doc/numpy-1.13.0/reference/routines.logic.html
other logical operators
logical_and(x1, x2, /[, out, where, ...]) Compute the truth value of
x1 AND x2 element-wise.
logical_or(x1, x2, /[, out, where, casting,
...]) Compute the truth value of x1 OR x2 element-wise.
logical_not(x, /[, out, where, casting, ...]) Compute the truth
value of NOT x element-wise.
logical_xor(x1, x2, /[, out, where, ..]) Compute the truth value of x1 XOR x2, element-wise.
You can use the method truncate
df = pd.DataFrame(np.random.rand(10, 5), columns = list('abcde'))
df_ab = df.truncate(before='a', after='b', axis=1)
df_cde = df.truncate(before='c', axis=1)
I have a big pandas dataframe from which I'm trying to select some rows with the .loc tool. The problem is that the condition I want to use in it needs an index which is given in one of the columns of the dataframe (the 'index' one). I try to select the row if the value is below a value that I need to found with the index in a simple list.
>>> df
r v index
1 2 2
2 4 3
3 20 1
>>> list
[3,6,32]
I want something like:
df.loc[ df['v'] < list[ df['index'] ] ]
So something which refers to the index in the studied row of the dataframe.
IIUC, convert the list to an array, and use "index" as the indexer:
v = np.array([3,6,32])
df[df['v'] < v[df['index'] - 1]]
r v index
0 1 2 2
1 2 4 3
Where,
v[df['index'] - 1]
# array([ 6, 32, 3])
r = df.loc[df['v'] < v[df['index'] - 1]].copy()