dataframe slice by list of values
In [1]: df = pd.DataFrame({'A': [5,6,3,4], 'B': [1,2,3,5]})
In [2]: df
Out[2]:
A B
0 5 1
1 6 2
2 3 3
3 4 5
In [3]: df[df['A'].isin([3, 6])]
Out[3]:
A B
1 6 2
2 3 3dataframe slice by list of values
In [1]: df = pd.DataFrame({'A': [5,6,3,4], 'B': [1,2,3,5]})
In [2]: df
Out[2]:
A B
0 5 1
1 6 2
2 3 3
3 4 5
In [3]: df[df['A'].isin([3, 6])]
Out[3]:
A B
1 6 2
2 3 3python - row slice dataframe by number of rows
df.iloc[[1, 5]] # Get rows 1 and 5
df.iloc[1:6] # Get rows 1 to 5 inclusive
df.iloc[[1, 5], df.columns.get_loc('Shop')] # Get only specific column
df.iloc[[1, 5], df.columns.get_indexer(['Shop', 'Category'])] # Get multiple columnspandas df by row index
indices = [133, 22, 19, 203, 14, 1]
df_by_indices = df.iloc[indices, :]add an index column in range dataframe
df = df.loc[df.index.repeat(df['a'])]
df['c'] = df.groupby(level=0).cumcount() + 1
df = df.reset_index(drop=True)
print (df)
a b c
0 1 x 1
1 2 y 1
2 2 y 2
3 3 z 1
4 3 z 2
5 3 z 3indexing column in pandas
In [8]: age_sex = titanic[["Age", "Sex"]]
In [9]: age_sex.head()
Out[9]:
Age Sex
0 22.0 male
1 38.0 female
2 26.0 female
3 35.0 female
4 35.0 maleCopyright © 2021 Codeinu
Forgot your account's password or having trouble logging into your Account? Don't worry, we'll help you to get back your account. Enter your email address and we'll send you a recovery link to reset your password. If you are experiencing problems resetting your password contact us
