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python list replace nan with 0

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Disgusted Dugong answered on April 11, 2020 Popularity 10/10 Helpfulness 4/10

answer python list replace nan with 0

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python list replace nan with 0

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mylist = [0 if x != x else x for x in mylist]

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Source: stackoverflow.com

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Contributed on Aug 12 2020

Disgusted Dugong

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how to replace nan values with 0 in pandas

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df.fillna(0)

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Contributed on Apr 11 2020

Bewildered Barracuda

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python dataframe replace nan with 0

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In [7]: df
Out[7]: 
          0         1
0       NaN       NaN
1 -0.494375  0.570994
2       NaN       NaN
3  1.876360 -0.229738
4       NaN       NaN

In [8]: df.fillna(0)
Out[8]: 
          0         1
0  0.000000  0.000000
1 -0.494375  0.570994
2  0.000000  0.000000
3  1.876360 -0.229738
4  0.000000  0.000000

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Source: stackoverflow.com

Tags: dataframe data

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Contributed on Apr 15 2020

Pleasant Panda

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replace nan with 0 pandas

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DataFrame.fillna()

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Contributed on Jul 07 2021

Adventurous Angelfish

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replace nan python

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# Check missing data
df.isna().any()
df.isna().sum()
# Visualize missing data information
import missingno as msno
import matplotlib.pyplot as plt
msno.matrix(df)
plt.show()

# Drop missing data column
df_dropped = df.dropna(subset = ['col'], axis = 1) # 0 for row
df.dropna(axis = 0) # Drop entire row for missing value (default)
df.dropna(axis = 1) # Drop entire column for missing value

# Replace/impute missing data with single value
col_mean = df['col'].mean()
df_imputed = df.fillna({'col': col_mean})
df.fillna(method='bfill')
df.interpolate(method='linear')
df['col'].replace(to_replace=np.nan, value = some_mean,inplace = True) # Alternative
# Replace/impute missing data with series
series_imp = df['col1'] * 5
df_imputed = df.fillna({'col2':series_imp})

df["col"].value_counts() # Look out for suspicious values

##### Strategic dropping example ########
# Drop missing values where <= 5% of data in column are missing , otherwise impute values
threshold = len(df) * 0.05
cols_to_drop = df.columns[df.isna().sum() <= threshold]
df.dropna(subset=cols_to_drop, inplace=True)
cols_with_missing_values = df.columns[salaries.isna().sum() > 0]
for col in cols_with_missing_values[:-1]:
    df[col].fillna(df[col].mode()[0])
subgroup_dict = df.groupby("cat_col")["num_col"].median().to_dict()
df["num_col"] = df["num_col"].fillna(df["cat_col"].map(subgroup_dict))

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Contributed on Feb 14 2023

Innocent Iguana

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python list replace nan with 0

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