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Restructuring DataFrame Based on Single Column Values

By : Joseph Cardoza
Date : October 18 2020, 03:08 PM
I wish this helpful for you I am trying to move data from one column to another based on multiple existing values. I researched and found a simple solution for a single column - as seen in the current code below. However, I would like a way to do it for all rows. I've been trying to research a way, but cannot seem to find a way to apply a possible loop to this function. Any help would be great. I am using the latest version of R, and RStudio. ! , Try the following:
code :
do.call(qpcR:::data.frame.na,split(df$X.People, df$Row))
   A  B C
1  3  1 3
2  2  1 3
3  2 NA 2
4 NA NA 1

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Create multiple columns based on values in single column in Pandas DataFrame

By : Prabhakar singh
Date : March 29 2020, 07:55 AM
will help you You can do this in many ways :
In pandas there is a function called pd.get_dummies() that allows you to convert each categorical data to a binary data. Apply it to your categorical column and then concatenate the dataframe obtained with the original one. Here is the link to the documentation.

Conditionally Replacing Values in a Dataframe Column Based on Values in Another Dataframe Column Using Tidyverse

By : Carl Nyden
Date : March 29 2020, 07:55 AM
Any of those help , Don't know if I get you right but maybe this works for you:
code :
df1[df1$Column2 == c("Cat", "Dog"), 1] <- "Course6"
df1 %>%
  spread(key = Column2, value = Column1) %>%
  mutate("Dog" = "Course6") %>%
  gather(key = "Column1", value = "Column2")

Join two data frame with two columns values of a df with a single column values of another dataframe. based on some cond

By : nikolay
Date : March 29 2020, 07:55 AM
I wish this helpful for you Use double merge with default inner join, for second filter out rows matched in df3, last concat together:
code :
df3 = df1.drop('col4', axis=1).merge(df2, on='col3')
df4 = (df1.drop('col3', axis=1).rename(columns={'col4':'col3'})
            .merge(df2[~df2['col3'].isin(df1['col3'])], on='col3'))

df = pd.concat([df3, df4],ignore_index=True)
print (df)
   col1  col2 col3  col5  col6
0     1     2    A     9    10
1     3     4    A     9    10
2     5     6    R    11    12
df3 = df1.drop('col4', axis=1).merge(df2, on='col3', how='left')
df4 = (df1.drop('col3', axis=1).rename(columns={'col4':'col3'})
            .merge(df2, on='col3', how='left'))

df = df3.combine_first(df4)
print (df)
   col1  col2 col3  col5  col6
0     1     2    A   9.0  10.0
1     3     4    A   9.0  10.0
2     5     6    B  11.0  12.0
3     7     8    B   NaN   NaN

Adding values to dataframe columns based on value from a single column

By : user3226472
Date : March 29 2020, 07:55 AM
help you fix your problem I have a DataFrame with yearly kg/ha values in which the columns reach from 1950 to 2015. The first column are gridcodes (gridcode_1) reaching from 1 to 4. Every gridcode has a specific addition value I want to add to the kg/ha values of the other columns.
code :
columns_list  = list(df.columns)

df.loc[(df['code'] == 1 ),columns_list] = df.loc[(df['code'] == 1),columns_list] + 2.7

df.loc[(df['code'] == 2 ),columns_list] = df.loc[(df['code'] == 2),columns_list] + 16.04

Creating Multiple DataFrames from single DataFrame based on different values of single column

By : user3300536
Date : March 29 2020, 07:55 AM
Hope that helps Unsure if your saving your variable into a csv or keep it in memory for further use,
you could pass each unique value into a dict and access by it's value :
code :
     Cal  Dates
0    85     23
1    75     23
2    74     23
3    97     23
4    54     24
5    10     24
6    77     24
7    95     24
8    58     25
9    53     25
10   44     25
11   94     25

d = {}

for frame, data in df.groupby('Dates'):
    d[f'df{frame}'] = data

    Cal  Dates
0   85     23
1   75     23
2   74     23
3   97     23
for k,v in d.items():
    i = (v['Cal'].loc[v['Cal'] > 70].count())
    print(f"{v['Dates'].unique()[0]} --> {i} times")
23 --> 4 times
24 --> 2 times
25 --> 1 times
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