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Python useful functions
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Pandas | |
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# Create dataframe from dictionary | |
df = pd.DataFrame.from_dict(data["trainData"]) | |
# Convert entire dataframe values to numeric type. | |
df = df.apply(pd.to_numeric, errors='ignore') | |
# Remove missing value. | |
df = df.dropna() | |
# Create a new column named "new_col" with value "2" if cluster_id column has value 1, else 0. | |
df.insert(loc=0, column=new_col, value=np.where(cluster_id == 1, 2, 0)) | |
# Create a new column named "new_col" with value of df["log2_livable"] if cluster_id is 1, else 0. | |
df.insert(loc=0, column=new_col, value=np.where(cluster_id == 1, df["log2_livable"], 0)) | |
# Change date format of entire column. | |
effective_date = pd.to_datetime(df["effective_date"], format='%Y-%m-%d') | |
# Re-order dataframe by columns (A-Z) | |
df = df[sorted(df.columns)] | |
# Clone dataframe | |
df.copy(deep=True) | |
# Fetch the row based on given index. | |
df.iloc[[index]] | |
# Fetch multiple columns. | |
df.loc[:, ['col_1', 'col_2']] | |
# Filter rows in dataframe where cluster_2 is 0 | |
X[X['cluster_2'].isin([0])].reset_index() | |
# Sum of rows | |
df = df.sum(axis = 1) | |
# Create dataframe from dictionary (Key as columns) | |
a={'b':100,'c':300} | |
pd.DataFrame(coefs, index=[0,]) |
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