Pandas .loc without KeyError

Update for @AlexLenail comment
It's a fair point that this will be slow for large lists. I did a little bit of more digging and found that the intersection method is available for Indexes and columns. I'm not sure about the algorithmic complexity but it's much faster empirically.

You can do something like this.

good_keys = df.index.intersection(all_keys)
df.loc[good_keys]

Or like your example

df = pd.DataFrame([1], index=['1'])
df.loc[df.index.intersection(['2'])]

Here is a little experiment below

n = 100000

# Create random values and random string indexes
# have the bad indexes contain extra values not in DataFrame Index
rand_val = np.random.rand(n)
rand_idx = []
for x in range(n):
    rand_idx.append(str(x))

bad_idx = []
for x in range(n*2):
    bad_idx.append(str(x))

df = pd.DataFrame(rand_val, index=rand_idx)
df.head()

def get_valid_keys_list_comp():
    # Return filtered DataFrame using list comprehension to filter keys
    vkeys = [key for key in bad_idx if key in df.index.values]
    return df.loc[vkeys]

def get_valid_keys_intersection():
    # Return filtered DataFrame using list intersection() to filter keys
    vkeys = df.index.intersection(bad_idx)
    return df.loc[vkeys]

%%timeit 
get_valid_keys_intersection()
# 64.5 ms ± 4.53 ms per loop (mean ± std. dev. of 7 runs, 10 loops each)

%%timeit 
get_valid_keys_list_comp()
# 6.14 s ± 457 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)

Original answer

I'm not sure if pandas has a built-in function to handle this but you can use Python list comprehension to filter to valid indexes with something like this.

Given a DataFrame df2

           A    B       C   D    F
test    1.0 2013-01-02  1.0 3   foo
train   1.0 2013-01-02  1.0 3   foo
test    1.0 2013-01-02  1.0 3   foo
train   1.0 2013-01-02  1.0 3   foo

You can filter your index query with this

keys = ['test', 'train', 'try', 'fake', 'broken']
valid_keys = [key for key in keys if key in df2.index.values]
df2.loc[valid_keys]

This will also work for columns if you use df2.columns instead of df2.index.values

I found an alternative (provided a check for df.empty is made beforehand). You could do something like this

df[df.index=='2'] -> returns either a dataframe with matched values or empty dataframe.

It seems to work fine for me. I'm running Python 3.5 with pandas version 0.20.3.

import numpy as np
import pandas as pd

# Create dataframe
data = {'distance': [0, 300, 600, 1000],
        'population': [4.8, 0.7, 6.4, 2.9]}
df = pd.DataFrame(data, index=['Alabama','Alaska','Arizona','Arkansas'])

keys = ['Alabama', 'Alaska', 'Arizona', 'Virginia']

# Create a subset of the dataframe.
df.loc[keys]
          distance  population
Alabama        0.0         4.8
Alaska       300.0         0.7
Arizona      600.0         6.4
Virginia       NaN         NaN

Or if you want to exclude the NaN row:

df.loc[keys].dropna()
          distance  population
Alabama        0.0         4.8
Alaska       300.0         0.7
Arizona      600.0         6.4

This page https://pandas.pydata.org/pandas-docs/stable/indexing.html#deprecate-loc-reindex-listlike has the solution:

In [8]: pd.DataFrame([1], index=['1']).reindex(['2']) Out[8]: 0 2 NaN

Using the sample dataframe from @binjip's answer:

import numpy as np
import pandas as pd

# Create dataframe
data = {'distance': [0, 300, 600, 1000],
        'population': [4.8, 0.7, 6.4, 2.9]}
df = pd.DataFrame(data, index=['Alabama','Alaska','Arizona','Arkansas'])

keys = ['Alabama', 'Alaska', 'Arizona', 'Virginia']

Get matching records from the dataframe. NB: The dataframe index must be unique for this to work!

df.reindex(keys)

          distance  population
Alabama        0.0         4.8
Alaska       300.0         0.7
Arizona      600.0         6.4
Virginia       NaN         NaN

If you want to omit missing keys:

df.reindex(df.index.intersection(keys))

         distance  population
Alabama         0         4.8
Alaska        300         0.7
Arizona       600         6.4