Filtering Single-Column Data Frames

how can I Filter single column in a dataframe on multiple values

Put all 61 MRNs into a list-

mrnList = [val1, val2, ...,val61]

Then filter these MRNs like-

df_filtered = df[df['MRN'].isin(mrnList)]

Keep note of your MRN value's datatype while making mrnList.

Returning a single column of Pandas dataframe *as a dataframe* after filtering on another column

Specify the condition(s) and the column(s) to return in on go with .loc:

df_filter = df.loc[df['price'] > 15, ['food']]

Output:

>>> df_filter
   food
1   ham
2  eggs

>>> type(df_filter)
pandas.core.frame.DataFrame

Filtering single-column data frames

Use the drop argument to the select functions:

single.c[single.c$col1 > 2, , drop = FALSE]

#   col1
#r3    3
#r4    4
#r5    5

From documentation for [:

drop

For matrices and arrays. If TRUE the result is coerced to the lowest
possible dimension (see the examples). This only works for extracting
elements, not for the replacement. See drop for further details.

Filtering a single column to two unique values

Use .isin() function, much easier and nicer to read

df[df['Time Type'].isin(['Full Time', 'Part Time'])]

filtering data in dataframe based on a column

Do you mean by something like?

>>> x = df.loc[(df['#3'].eq(10) & df['#3'].shift(-1).eq(21)) | (df['#3'].shift().eq(10) & df['#3'].eq(21))]
>>> x.groupby(np.arange(len(x)) // 2).apply(lambda x: pd.DataFrame(x.drop(["#1", "#3"], axis=1).to_numpy().flatten()).T).reset_index(drop=True).rename(lambda x: f'#{x + 1}', axis=1)
     #1   #2    #3   #4
0   Bob  Eng  Jack  Tel
1  Rams  Mal  Venk  Mar
>>> 

Pandas Filtering Multiple Conditions on Single Column

I think using pd.to_datetime and pd.Series.between should work for you:

filtered_df = df[pd.to_datetime(df['Login Date'].str.split(' ').str[0], format="%Y/%m/%d").between(semester_start, semester_end)]

Filter rows based on one column from a list of dataframes

idx <- "->"

# Base R
lapply(dfs, function(df) df[df$v1 == "->",])
lapply(dfs, function(df) df[df$v1 %in% idx,])

# tidyverse

library("purrr")
library("dplyr")

map(dfs, filter, v1 == "->")
map(dfs, filter, v1 %in% !! idx)

---

Related Topics