Conditional Replacement of Values in a Data.Frame

Conditional Replace Pandas

.ix indexer works okay for pandas version prior to 0.20.0, but since pandas 0.20.0, the .ix indexer is deprecated, so you should avoid using it. Instead, you can use .loc or iloc indexers. You can solve this problem by:

mask = df.my_channel > 20000
column_name = 'my_channel'
df.loc[mask, column_name] = 0

Or, in one line,

df.loc[df.my_channel > 20000, 'my_channel'] = 0

mask helps you to select the rows in which df.my_channel > 20000 is True, while df.loc[mask, column_name] = 0 sets the value 0 to the selected rows where maskholds in the column which name is column_name.

Update:
In this case, you should use loc because if you use iloc, you will get a NotImplementedError telling you that iLocation based boolean indexing on an integer type is not available.

Pandas: Conditionally replace values based on other columns values

Now my goal is for each add_rd in the event column, the associated
NaN-value in the environment column should be replaced with a string
RD.

As per @Zero's comment, use pd.DataFrame.loc and Boolean indexing:

df.loc[df['event'].eq('add_rd') & df['environment'].isnull(), 'environment'] = 'RD'

Conditionally replace certain values in my dataframe with other values in the dataframe in R

Using your sample data:

library(dplyr)
library(tidyr)

df %>% 
  replace(. == "NA", NA_character_) %>% 
  group_by(studyID) %>% 
  fill(c(q1,q11,q2B,q2C,q2a,q4,q9),.direction = "down")

This gives us:

# A tibble: 9 x 9
# Groups:   studyID [4]
  studyID effect            q1    q11   q2B   q2C   q2a   q4    q9   
  <chr>   <chr>             <chr> <chr> <chr> <chr> <chr> <chr> <chr>
1 s100    All.outcomes      low   NA    low   high  low   low   NA   
2 s100    Study.1..Effect.1 low   high  low   high  low   low   low  
3 s100    Study.1..Effect.2 low   high  low   high  low   low   low  
4 s101    All.outcomes      low   low   low   high  low   low   low  
5 s102    All.outcomes      low   low   low   high  low   high  low  
6 s104    All.outcomes      low   NA    low   high  low   low   NA   
7 s104    Study.1..Effect.1 low   low   low   high  low   low   low  
8 s104    Study.2..Effect.1 low   high  low   high  low   low   high 
9 s104    Study.3..Effect.1 low   low   low   high  low   low   low  

How to conditionally replace values in a dataframe?

Use Series.map only for values filtered by boolean indexing:

#added values for match
lookup_dict = {'13:00:00':1, '16:00:00':2, '23:00:00':0}

m = df['XX'] == -1
df.loc[m, 'XX'] = df.loc[m, 'Time'].map(lookup_dict)
print (df)
   XX        Date      Time
0   0  2016-05-01  19:00:00
1   1  2016-05-01  18:00:00
2   3  2016-05-01  17:00:00
3   2  2016-05-01  16:00:00
4   5  2016-05-01  15:00:00
5   7  2016-05-01  14:00:00
6   1  2016-05-01  13:00:00
7   6  2016-05-01  12:00:00

Replace all values in a data frame, conditionally

library(tidyverse)

df <- data.frame(
  var1 = c(2L, 3L, 5L),
  var2 = c(3L, 6L, 3L),
  var3 = c(5L, 8L, 7L),
  var4 = c(8L, 7L, 4L)
)

df %>%
  mutate(across(.fns = ~ . >= 4)) %>%
  summarise(across(.fns = ~ sum(.)/length(.) ))
#>        var1      var2 var3 var4
#> 1 0.3333333 0.3333333    1    1

Pandas DataFrame: replace all values in a column, based on condition

You need to select that column:

In [41]:
df.loc[df['First Season'] > 1990, 'First Season'] = 1
df

Out[41]:
                 Team  First Season  Total Games
0      Dallas Cowboys          1960          894
1       Chicago Bears          1920         1357
2   Green Bay Packers          1921         1339
3      Miami Dolphins          1966          792
4    Baltimore Ravens             1          326
5  San Franciso 49ers          1950         1003

So the syntax here is:

df.loc[<mask>(here mask is generating the labels to index) , <optional column(s)> ]

You can check the docs and also the 10 minutes to pandas which shows the semantics

EDIT

If you want to generate a boolean indicator then you can just use the boolean condition to generate a boolean Series and cast the dtype to int this will convert True and False to 1 and 0 respectively:

In [43]:
df['First Season'] = (df['First Season'] > 1990).astype(int)
df

Out[43]:
                 Team  First Season  Total Games
0      Dallas Cowboys             0          894
1       Chicago Bears             0         1357
2   Green Bay Packers             0         1339
3      Miami Dolphins             0          792
4    Baltimore Ravens             1          326
5  San Franciso 49ers             0         1003

Conditional replacement of values in dataframe with NA

Using the code that GenesRus handed me, I was able to modify the code to select the trials that I want:

trialdata_filter <- trialdata %>%
  mutate(direction= as.logical(direction)) %>% 
  mutate(is.special = case_when(direction == FALSE & Y > 180 ~ TRUE, direction == TRUE & Y <20 ~ TRUE, TRUE ~ FALSE)) %>% 
  group_by(bartrial) %>%
  filter(!any(is.special[1:25] == TRUE))

Thanks for the help!

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